{"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/decoder/papers/26","list_of":"/task/decoder","task":"Decoder","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":26,"pages_in_order":104,"rows_per_page":100,"rows":[2501,2600],"of":10368,"counts":{"archive_papers_tagged":10368,"with_a_code_link":4358,"where_syntology_ran_a_sample":1061,"not_listed_spam_title":0,"listed":10368,"listed_where_code_ran":1061,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":152,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":152,"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/decoder","prev":"/task/decoder/papers/25","next":"/task/decoder/papers/27","papers":[{"url":"/paper/lmqformer-a-laplace-prior-guided-mask-query","slug":"lmqformer-a-laplace-prior-guided-mask-query","title":"LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal","date":"2022-10-10","arxiv_id":"2210.04787","repositories_listed":1,"syntology":null},{"url":"/paper/mmt-image-guided-story-ending-generation-with","slug":"mmt-image-guided-story-ending-generation-with","title":"MMT: Image-guided Story Ending Generation with Multimodal Memory Transformer","date":"2022-10-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improved-abdominal-multi-organ-segmentation","slug":"improved-abdominal-multi-organ-segmentation","title":"Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks","date":"2022-10-09","arxiv_id":"2210.04285","repositories_listed":1,"syntology":null},{"url":"/paper/arabsign-a-multi-modality-dataset-and","slug":"arabsign-a-multi-modality-dataset-and","title":"ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition","date":"2022-10-08","arxiv_id":"2210.03951","repositories_listed":1,"syntology":null},{"url":"/paper/granite-a-graph-neural-network-model-for","slug":"granite-a-graph-neural-network-model-for","title":"GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation","date":"2022-10-08","arxiv_id":"2210.03894","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-encoder-decoder-framework-with","slug":"a-unified-encoder-decoder-framework-with","title":"A Unified Encoder-Decoder Framework with Entity Memory","date":"2022-10-07","arxiv_id":"2210.03273","repositories_listed":1,"syntology":null},{"url":"/paper/an-energy-efficient-spiking-neural-network","slug":"an-energy-efficient-spiking-neural-network","title":"An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface","date":"2022-10-07","arxiv_id":"2210.06287","repositories_listed":1,"syntology":null},{"url":"/paper/embryosformer-deformable-transformer-and","slug":"embryosformer-deformable-transformer-and","title":"EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification","date":"2022-10-07","arxiv_id":"2210.04615","repositories_listed":1,"syntology":null},{"url":"/paper/a-distributional-lens-for-multi-aspect","slug":"a-distributional-lens-for-multi-aspect","title":"A Distributional Lens for Multi-Aspect Controllable Text Generation","date":"2022-10-06","arxiv_id":"2210.02889","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-distributional-lens-for-multi-aspect#ran","syntology_url":"https://syntology.ai/paper/2210.02889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02889"}},"official":{"repos":["happygu0524/multicontrol"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-double-encoder-network-for-rgb-d","slug":"robust-double-encoder-network-for-rgb-d","title":"Robust Double-Encoder Network for RGB-D Panoptic Segmentation","date":"2022-10-06","arxiv_id":"2210.02834","repositories_listed":1,"syntology":null},{"url":"/paper/contextualized-generative-retrieval","slug":"contextualized-generative-retrieval","title":"Nonparametric Decoding for Generative Retrieval","date":"2022-10-05","arxiv_id":"2210.02068","repositories_listed":1,"syntology":null},{"url":"/paper/joeys2t-minimalistic-speech-to-text-modeling","slug":"joeys2t-minimalistic-speech-to-text-modeling","title":"JoeyS2T: Minimalistic Speech-to-Text Modeling with JoeyNMT","date":"2022-10-05","arxiv_id":"2210.02545","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/joeys2t-minimalistic-speech-to-text-modeling#ran","syntology_url":"https://syntology.ai/paper/2210.02545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02545"}},"official":{"repos":["may-/joeys2t"],"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/tripletformer-for-probabilistic-interpolation","slug":"tripletformer-for-probabilistic-interpolation","title":"Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series","date":"2022-10-05","arxiv_id":"2210.02091","repositories_listed":1,"syntology":null},{"url":"/paper/apaunet-axis-projection-attention-unet-for","slug":"apaunet-axis-projection-attention-unet-for","title":"APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation","date":"2022-10-04","arxiv_id":"2210.01485","repositories_listed":1,"syntology":null},{"url":"/paper/towards-improving-faithfulness-in-abstractive","slug":"towards-improving-faithfulness-in-abstractive","title":"Towards Improving Faithfulness in Abstractive Summarization","date":"2022-10-04","arxiv_id":"2210.01877","repositories_listed":1,"syntology":null},{"url":"/paper/benign-autoencoders","slug":"benign-autoencoders","title":"Benign Autoencoders","date":"2022-10-02","arxiv_id":"2210.00637","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-more-information-in-sparse-point","slug":"exploiting-more-information-in-sparse-point","title":"Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking","date":"2022-10-02","arxiv_id":"2210.00519","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-contrastive-learning-for-1","slug":"fine-grained-contrastive-learning-for-1","title":"Fine-grained Contrastive Learning for Definition Generation","date":"2022-10-02","arxiv_id":"2210.00543","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-legal-judgment-prediction-with","slug":"augmenting-legal-judgment-prediction-with","title":"Augmenting Legal Judgment Prediction with Contrastive Case Relations","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/crisp-curriculum-based-sequential-neural","slug":"crisp-curriculum-based-sequential-neural","title":"CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family","date":"2022-10-01","arxiv_id":"2210.00313","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"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) · 2 unverified","sample_list":"/paper/crisp-curriculum-based-sequential-neural#ran","syntology_url":"https://syntology.ai/paper/2210.00313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00313"}},"official":{"repos":["hebbarashwin/neural_polar_decoder"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/quantifying-bias-from-decoding-techniques-in","slug":"quantifying-bias-from-decoding-techniques-in","title":"Quantifying Bias from Decoding Techniques in Natural Language Generation","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ruleformer-context-aware-rule-mining-over","slug":"ruleformer-context-aware-rule-mining-over","title":"Ruleformer: Context-aware Rule Mining over Knowledge Graph","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-encoder-decoder-architecture","slug":"an-efficient-encoder-decoder-architecture","title":"An efficient encoder-decoder architecture with top-down attention for speech separation","date":"2022-09-30","arxiv_id":"2209.15200","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":8,"n_ran_checked":9,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/an-efficient-encoder-decoder-architecture#ran","syntology_url":"https://syntology.ai/paper/2209.15200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.15200"}},"official":{"repos":["JusperLee/TDANet"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/smallcap-lightweight-image-captioning","slug":"smallcap-lightweight-image-captioning","title":"SmallCap: Lightweight Image Captioning Prompted with Retrieval Augmentation","date":"2022-09-30","arxiv_id":"2209.15323","repositories_listed":1,"syntology":null},{"url":"/paper/tinyturbo-efficient-turbo-decoders-on-edge","slug":"tinyturbo-efficient-turbo-decoders-on-edge","title":"TinyTurbo: Efficient Turbo Decoders on Edge","date":"2022-09-30","arxiv_id":"2209.15614","repositories_listed":1,"syntology":null},{"url":"/paper/facial-landmark-predictions-with-applications","slug":"facial-landmark-predictions-with-applications","title":"Facial Landmark Predictions with Applications to Metaverse","date":"2022-09-29","arxiv_id":"2209.14698","repositories_listed":1,"syntology":null},{"url":"/paper/training-b-vae-by-aggregating-a-learned","slug":"training-b-vae-by-aggregating-a-learned","title":"Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder","date":"2022-09-29","arxiv_id":"2209.14783","repositories_listed":1,"syntology":null},{"url":"/paper/embracing-consistency-a-one-stage-approach","slug":"embracing-consistency-a-one-stage-approach","title":"Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding","date":"2022-09-27","arxiv_id":"2209.13306","repositories_listed":1,"syntology":{"n":18,"n_ran":11,"n_constructed":9,"n_ran_checked":10,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 ran (of which 9 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/embracing-consistency-a-one-stage-approach#ran","syntology_url":"https://syntology.ai/paper/2209.13306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.13306"}},"official":{"repos":["jy0205/stcat"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":10,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/lex2sent-a-bagging-approach-to-unsupervised-1","slug":"lex2sent-a-bagging-approach-to-unsupervised-1","title":"Lex2Sent: A bagging approach to unsupervised sentiment analysis","date":"2022-09-26","arxiv_id":"2209.13023","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-segment-assemblage-network-for-ad","slug":"multi-modal-segment-assemblage-network-for-ad","title":"Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward","date":"2022-09-25","arxiv_id":"2209.12164","repositories_listed":1,"syntology":null},{"url":"/paper/prodesign-toward-effective-and-efficient","slug":"prodesign-toward-effective-and-efficient","title":"PiFold: Toward effective and efficient protein inverse folding","date":"2022-09-22","arxiv_id":"2209.12643","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/prodesign-toward-effective-and-efficient#ran","syntology_url":"https://syntology.ai/paper/2209.12643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.12643"}},"official":{"repos":["A4Bio/PiFold"],"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/compressing-sign-information-in-dct-based","slug":"compressing-sign-information-in-dct-based","title":"Compressing Sign Information in DCT-based Image Coding via Deep Sign Retrieval","date":"2022-09-21","arxiv_id":"2209.10712","repositories_listed":1,"syntology":null},{"url":"/paper/relaxed-attention-for-transformer-models","slug":"relaxed-attention-for-transformer-models","title":"Relaxed Attention for Transformer Models","date":"2022-09-20","arxiv_id":"2209.09735","repositories_listed":1,"syntology":null},{"url":"/paper/vitag-online-wifi-fine-time-measurements","slug":"vitag-online-wifi-fine-time-measurements","title":"ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments","date":"2022-09-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/niert-accurate-numerical-interpolation","slug":"niert-accurate-numerical-interpolation","title":"NIERT: Accurate Numerical Interpolation through Unifying Scattered Data Representations using Transformer Encoder","date":"2022-09-19","arxiv_id":"2209.09078","repositories_listed":1,"syntology":null},{"url":"/paper/delving-globally-into-texture-and-structure","slug":"delving-globally-into-texture-and-structure","title":"Delving Globally into Texture and Structure for Image Inpainting","date":"2022-09-17","arxiv_id":"2209.08217","repositories_listed":1,"syntology":null},{"url":"/paper/answering-numerical-reasoning-questions-in","slug":"answering-numerical-reasoning-questions-in","title":"Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder","date":"2022-09-16","arxiv_id":"2209.07692","repositories_listed":1,"syntology":null},{"url":"/paper/gatraj-a-graph-and-attention-based-multi","slug":"gatraj-a-graph-and-attention-based-multi","title":"GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model","date":"2022-09-16","arxiv_id":"2209.07857","repositories_listed":1,"syntology":null},{"url":"/paper/a-robotic-visual-grasping-design-rethinking","slug":"a-robotic-visual-grasping-design-rethinking","title":"A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions","date":"2022-09-15","arxiv_id":"2209.07459","repositories_listed":1,"syntology":null},{"url":"/paper/stateful-memory-augmented-transformers-for","slug":"stateful-memory-augmented-transformers-for","title":"Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling","date":"2022-09-15","arxiv_id":"2209.07634","repositories_listed":1,"syntology":null},{"url":"/paper/stpotr-simultaneous-human-trajectory-and-pose","slug":"stpotr-simultaneous-human-trajectory-and-pose","title":"STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead","date":"2022-09-15","arxiv_id":"2209.07600","repositories_listed":1,"syntology":null},{"url":"/paper/lossy-image-compression-with-conditional-1","slug":"lossy-image-compression-with-conditional-1","title":"Lossy Image Compression with Conditional Diffusion Models","date":"2022-09-14","arxiv_id":"2209.06950","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 2 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lossy-image-compression-with-conditional-1#ran","syntology_url":"https://syntology.ai/paper/2209.06950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06950"}},"official":{"repos":["buggyyang/cdc_compression"],"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/pali-a-jointly-scaled-multilingual-language","slug":"pali-a-jointly-scaled-multilingual-language","title":"PaLI: A Jointly-Scaled Multilingual Language-Image Model","date":"2022-09-14","arxiv_id":"2209.06794","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 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) · 2 unverified","sample_list":"/paper/pali-a-jointly-scaled-multilingual-language#ran","syntology_url":"https://syntology.ai/paper/2209.06794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06794"}},"official":{"repos":["google-research/big_vision"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-language-foundation-models-for","slug":"leveraging-language-foundation-models-for","title":"Leveraging Language Foundation Models for Human Mobility Forecasting","date":"2022-09-11","arxiv_id":"2209.05479","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/leveraging-language-foundation-models-for#ran","syntology_url":"https://syntology.ai/paper/2209.05479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05479"}},"official":{"repos":["cruiseresearchgroup/AuxMobLCast"],"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/revisiting-active-sets-for-gaussian-process","slug":"revisiting-active-sets-for-gaussian-process","title":"Revisiting Active Sets for Gaussian Process Decoders","date":"2022-09-10","arxiv_id":"2209.04636","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/revisiting-active-sets-for-gaussian-process#ran","syntology_url":"https://syntology.ai/paper/2209.04636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.04636"}},"official":{"repos":["pmorenoz/SASGP"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-document-scientific-summarization-from","slug":"multi-document-scientific-summarization-from","title":"Multi-Document Scientific Summarization from a Knowledge Graph-Centric View","date":"2022-09-09","arxiv_id":"2209.04319","repositories_listed":1,"syntology":null},{"url":"/paper/crosslink-net-double-branch-encoder-network","slug":"crosslink-net-double-branch-encoder-network","title":"Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation","date":"2022-09-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/spach-transformer-spatial-and-channel-wise","slug":"spach-transformer-spatial-and-channel-wise","title":"Spach Transformer: Spatial and Channel-wise Transformer Based on Local and Global Self-attentions for PET Image Denoising","date":"2022-09-07","arxiv_id":"2209.03300","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-bidirectional-unsupervised","slug":"multilingual-bidirectional-unsupervised","title":"Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation","date":"2022-09-06","arxiv_id":"2209.02821","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-temporal-transformer-for-video","slug":"spatial-temporal-transformer-for-video","title":"Spatial-Temporal Transformer for Video Snapshot Compressive Imaging","date":"2022-09-04","arxiv_id":"2209.01578","repositories_listed":1,"syntology":null},{"url":"/paper/focus-driven-contrastive-learniang-for","slug":"focus-driven-contrastive-learniang-for","title":"Focus-Driven Contrastive Learniang for Medical Question Summarization","date":"2022-09-01","arxiv_id":"2209.00484","repositories_listed":1,"syntology":null},{"url":"/paper/histoseg-quick-attention-with-multi-loss","slug":"histoseg-quick-attention-with-multi-loss","title":"HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images","date":"2022-09-01","arxiv_id":"2209.00729","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/lexmae-lexicon-bottlenecked-pretraining-for","slug":"lexmae-lexicon-bottlenecked-pretraining-for","title":"LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval","date":"2022-08-31","arxiv_id":"2208.14754","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"6 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lexmae-lexicon-bottlenecked-pretraining-for#ran","syntology_url":"https://syntology.ai/paper/2208.14754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.14754"}},"official":{"repos":["taoshen58/lexmae"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/nestedformer-nested-modality-aware","slug":"nestedformer-nested-modality-aware","title":"NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation","date":"2022-08-31","arxiv_id":"2208.14876","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/nestedformer-nested-modality-aware#ran","syntology_url":"https://syntology.ai/paper/2208.14876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.14876"}},"official":{"repos":["920232796/nestedformer"],"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/denoising-architecture-for-unsupervised","slug":"denoising-architecture-for-unsupervised","title":"Denoising Architecture for Unsupervised Anomaly Detection in Time-Series","date":"2022-08-30","arxiv_id":"2208.14337","repositories_listed":1,"syntology":null},{"url":"/paper/modnet-multi-offset-point-cloud-denoising","slug":"modnet-multi-offset-point-cloud-denoising","title":"MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches","date":"2022-08-30","arxiv_id":"2208.14160","repositories_listed":1,"syntology":null},{"url":"/paper/tailoring-molecules-for-protein-pockets-a","slug":"tailoring-molecules-for-protein-pockets-a","title":"Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design","date":"2022-08-30","arxiv_id":"2209.06158","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/tailoring-molecules-for-protein-pockets-a#ran","syntology_url":"https://syntology.ai/paper/2209.06158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06158"}},"official":{"repos":["hankerwu/tamgent"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/boundary-aware-network-for-abdominal-multi","slug":"boundary-aware-network-for-abdominal-multi","title":"Boundary-Aware Network for Abdominal Multi-Organ Segmentation","date":"2022-08-29","arxiv_id":"2208.13774","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-aware-network-for-kidney-parsing","slug":"boundary-aware-network-for-kidney-parsing","title":"Boundary-Aware Network for Kidney Parsing","date":"2022-08-29","arxiv_id":"2208.13338","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-cross-architecture-black-box","slug":"cross-domain-cross-architecture-black-box","title":"Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies","date":"2022-08-28","arxiv_id":"2208.13182","repositories_listed":1,"syntology":null},{"url":"/paper/latent-signal-models-learning-compact","slug":"latent-signal-models-learning-compact","title":"Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI","date":"2022-08-27","arxiv_id":"2208.13003","repositories_listed":1,"syntology":null},{"url":"/paper/laplacian-pyramid-like-autoencoder","slug":"laplacian-pyramid-like-autoencoder","title":"Laplacian Pyramid-like Autoencoder","date":"2022-08-26","arxiv_id":"2208.12484","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":2,"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/laplacian-pyramid-like-autoencoder#ran","syntology_url":"https://syntology.ai/paper/2208.12484","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12484"}},"official":{"repos":["sangjun7/lpae"],"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/source-camera-identification-with-multi-scale","slug":"source-camera-identification-with-multi-scale","title":"Source Camera Identification with Multi-Scale Feature Fusion Network","date":"2022-08-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/user-controllable-latent-transformer-for","slug":"user-controllable-latent-transformer-for","title":"User-Controllable Latent Transformer for StyleGAN Image Layout Editing","date":"2022-08-26","arxiv_id":"2208.12408","repositories_listed":1,"syntology":null},{"url":"/paper/vmformer-end-to-end-video-matting-with","slug":"vmformer-end-to-end-video-matting-with","title":"VMFormer: End-to-End Video Matting with Transformer","date":"2022-08-26","arxiv_id":"2208.12801","repositories_listed":1,"syntology":null},{"url":"/paper/multimedia-generative-script-learning-for","slug":"multimedia-generative-script-learning-for","title":"Multimedia Generative Script Learning for Task Planning","date":"2022-08-25","arxiv_id":"2208.12306","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/multimedia-generative-script-learning-for#ran","syntology_url":"https://syntology.ai/paper/2208.12306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12306"}},"official":{"repos":["EagleW/Multimedia-Generative-Script-Learning-for-Task-Planning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/the-reprgesture-entry-to-the-genea-challenge","slug":"the-reprgesture-entry-to-the-genea-challenge","title":"The ReprGesture entry to the GENEA Challenge 2022","date":"2022-08-25","arxiv_id":"2208.12133","repositories_listed":1,"syntology":null},{"url":"/paper/distance-aware-occlusion-detection-with","slug":"distance-aware-occlusion-detection-with","title":"Distance-Aware Occlusion Detection with Focused Attention","date":"2022-08-23","arxiv_id":"2208.11122","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/distance-aware-occlusion-detection-with#ran","syntology_url":"https://syntology.ai/paper/2208.11122","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11122"}},"official":{"repos":["yang-li-2000/distance-aware-occlusion-detection-with-focused-attention"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-user-behavior-sequence-modeling-by","slug":"enhancing-user-behavior-sequence-modeling-by","title":"Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search","date":"2022-08-23","arxiv_id":"2208.10846","repositories_listed":1,"syntology":null},{"url":"/paper/string-based-molecule-generation-via-multi","slug":"string-based-molecule-generation-via-multi","title":"String-based Molecule Generation via Multi-decoder VAE","date":"2022-08-23","arxiv_id":"2208.10718","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-planning-in-a-compact-latent-action","slug":"efficient-planning-in-a-compact-latent-action","title":"Efficient Planning in a Compact Latent Action Space","date":"2022-08-22","arxiv_id":"2208.10291","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/efficient-planning-in-a-compact-latent-action#ran","syntology_url":"https://syntology.ai/paper/2208.10291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.10291"}},"official":{"repos":["ZhengyaoJiang/latentplan"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/instanceformer-an-online-video-instance","slug":"instanceformer-an-online-video-instance","title":"InstanceFormer: An Online Video Instance Segmentation Framework","date":"2022-08-22","arxiv_id":"2208.10547","repositories_listed":1,"syntology":null},{"url":"/paper/learning-low-bending-and-low-distortion-1","slug":"learning-low-bending-and-low-distortion-1","title":"Convergent autoencoder approximation of low bending and low distortion manifold embeddings","date":"2022-08-22","arxiv_id":"2208.10193","repositories_listed":1,"syntology":null},{"url":"/paper/z-code-a-pre-trained-language-model-optimized","slug":"z-code-a-pre-trained-language-model-optimized","title":"Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization","date":"2022-08-21","arxiv_id":"2208.09770","repositories_listed":1,"syntology":null},{"url":"/paper/aspect-based-sentiment-classification-with-1","slug":"aspect-based-sentiment-classification-with-1","title":"Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph","date":"2022-08-19","arxiv_id":"2208.09417","repositories_listed":1,"syntology":null},{"url":"/paper/curbing-task-interference-using","slug":"curbing-task-interference-using","title":"Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing","date":"2022-08-19","arxiv_id":"2208.09427","repositories_listed":1,"syntology":null},{"url":"/paper/diverse-video-captioning-by-adaptive-spatio","slug":"diverse-video-captioning-by-adaptive-spatio","title":"Diverse Video Captioning by Adaptive Spatio-temporal Attention","date":"2022-08-19","arxiv_id":"2208.09266","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-limits-of-synthetic-creation-of","slug":"exploring-the-limits-of-synthetic-creation-of","title":"Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation","date":"2022-08-19","arxiv_id":"2208.09512","repositories_listed":1,"syntology":null},{"url":"/paper/ahead-a-triple-attention-based-heterogeneous","slug":"ahead-a-triple-attention-based-heterogeneous","title":"AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach","date":"2022-08-17","arxiv_id":"2208.08200","repositories_listed":1,"syntology":null},{"url":"/paper/cat-beyond-efficient-transformer-for-content","slug":"cat-beyond-efficient-transformer-for-content","title":"CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences","date":"2022-08-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/infrared-small-uav-target-detection-based-on","slug":"infrared-small-uav-target-detection-based-on","title":"Infrared Small UAV Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion","date":"2022-08-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/milan-masked-image-pretraining-on-language","slug":"milan-masked-image-pretraining-on-language","title":"MILAN: Masked Image Pretraining on Language Assisted Representation","date":"2022-08-11","arxiv_id":"2208.06049","repositories_listed":1,"syntology":null},{"url":"/paper/arbitrary-point-cloud-upsampling-with","slug":"arbitrary-point-cloud-upsampling-with","title":"Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussians","date":"2022-08-10","arxiv_id":"2208.05274","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-diverse-chemical-reactions-for","slug":"modeling-diverse-chemical-reactions-for","title":"Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables","date":"2022-08-10","arxiv_id":"2208.05482","repositories_listed":1,"syntology":null},{"url":"/paper/asr-error-correction-with-constrained","slug":"asr-error-correction-with-constrained","title":"ASR Error Correction with Constrained Decoding on Operation Prediction","date":"2022-08-09","arxiv_id":"2208.04641","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-residual-learning-based-vector","slug":"hierarchical-residual-learning-based-vector","title":"Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation","date":"2022-08-09","arxiv_id":"2208.04554","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-robustness-of-b-vae-through-the","slug":"adversarial-robustness-of-b-vae-through-the","title":"Adversarial robustness of VAEs through the lens of local geometry","date":"2022-08-08","arxiv_id":"2208.03923","repositories_listed":1,"syntology":null},{"url":"/paper/generating-coherent-narratives-by-learning","slug":"generating-coherent-narratives-by-learning","title":"Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework","date":"2022-08-08","arxiv_id":"2208.03985","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-resolution-and-degradation-clues-as","slug":"exploring-resolution-and-degradation-clues-as","title":"Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection","date":"2022-08-05","arxiv_id":"2208.03062","repositories_listed":1,"syntology":null},{"url":"/paper/transdssl-transformer-based-depth-estimation","slug":"transdssl-transformer-based-depth-estimation","title":"TransDSSL: Transformer based Depth Estimation via Self-Supervised Learning","date":"2022-08-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/transmatting-enhancing-transparent-objects","slug":"transmatting-enhancing-transparent-objects","title":"TransMatting: Enhancing Transparent Objects Matting with Transformers","date":"2022-08-05","arxiv_id":"2208.03007","repositories_listed":1,"syntology":null},{"url":"/paper/utopic-uncertainty-aware-overlap-prediction","slug":"utopic-uncertainty-aware-overlap-prediction","title":"UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration","date":"2022-08-04","arxiv_id":"2208.02712","repositories_listed":1,"syntology":null},{"url":"/paper/neural-contourlet-network-for-monocular-360","slug":"neural-contourlet-network-for-monocular-360","title":"Neural Contourlet Network for Monocular 360 Depth Estimation","date":"2022-08-03","arxiv_id":"2208.01817","repositories_listed":1,"syntology":null},{"url":"/paper/ssformer-a-lightweight-transformer-for","slug":"ssformer-a-lightweight-transformer-for","title":"SSformer: A Lightweight Transformer for Semantic Segmentation","date":"2022-08-03","arxiv_id":"2208.02034","repositories_listed":1,"syntology":null},{"url":"/paper/alexatm-20b-few-shot-learning-using-a-large","slug":"alexatm-20b-few-shot-learning-using-a-large","title":"AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model","date":"2022-08-02","arxiv_id":"2208.01448","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/alexatm-20b-few-shot-learning-using-a-large#ran","syntology_url":"https://syntology.ai/paper/2208.01448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.01448"}},"official":null}},{"url":"/paper/itermiunet-a-lightweight-architecture-for","slug":"itermiunet-a-lightweight-architecture-for","title":"IterMiUnet: A lightweight architecture for automatic blood vessel segmentation","date":"2022-08-02","arxiv_id":"2208.01485","repositories_listed":1,"syntology":null},{"url":"/paper/streaming-capable-high-performance","slug":"streaming-capable-high-performance","title":"Streaming-capable High-performance Architecture of Learned Image Compression Codecs","date":"2022-08-02","arxiv_id":"2208.01641","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-long-text-understanding-with-short","slug":"efficient-long-text-understanding-with-short","title":"Efficient Long-Text Understanding with Short-Text Models","date":"2022-08-01","arxiv_id":"2208.00748","repositories_listed":1,"syntology":null},{"url":"/paper/glean-generative-latent-bank-for-image-super","slug":"glean-generative-latent-bank-for-image-super","title":"GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond","date":"2022-07-29","arxiv_id":"2207.14812","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":0,"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/glean-generative-latent-bank-for-image-super#ran","syntology_url":"https://syntology.ai/paper/2207.14812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14812"}},"official":{"repos":["open-mmlab/mmediting"],"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"]}}}],"record_sha256":"3641c4623a6908e5fa23a5dc591e2c079c3aa477816bd60e5dfce55feba865d1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}