{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/get-rank","entry":"get_rank","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":42,"n_papers_ran":17,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":34,"n_samples_ran":16,"n_samples_fingerprinted":2,"n_places":42,"n_places_pointer_only":14,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":13,"unverified":18},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.29706","paper":"/paper/arxiv-2606-29706","title":"ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"heshandevaka/ARMOR","path":"retriever_training/train_armor.py","file_url":"https://github.com/heshandevaka/ARMOR/blob/HEAD/retriever_training/train_armor.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d4209866bbf0ccb4","mcp_get_code":{"code_sha256":"d4209866bbf0ccb4"}},{"arxiv_id":"2506.20640","paper":"/paper/towards-community-driven-agents-for-machine","title":"Towards Community-Driven Agents for Machine Learning Engineering","date":"2025-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"comind-ml/comind","path":"comind/kaggle/leaderboard.py","file_url":"https://github.com/comind-ml/comind/blob/HEAD/comind/kaggle/leaderboard.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c374cab52534944","mcp_get_code":{"code_sha256":"3c374cab52534944"}},{"arxiv_id":"2501.00656","paper":"/paper/2-olmo-2-furious","title":"2 OLMo 2 Furious","date":"2024-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/OLMo-core","path":"src/olmo_core/distributed/utils.py","file_url":"https://github.com/allenai/OLMo-core/blob/HEAD/src/olmo_core/distributed/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b32994fbeacb1e9b","mcp_get_code":{"code_sha256":"b32994fbeacb1e9b"}},{"arxiv_id":"2411.07404","paper":"/paper/controllable-context-sensitivity-and-the-knob","title":"Controllable Context Sensitivity and the Knob Behind It","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdu4108/context-vs-prior-finetuning","path":"analysis/circuit_utils/decoding.py","file_url":"https://github.com/kdu4108/context-vs-prior-finetuning/blob/HEAD/analysis/circuit_utils/decoding.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"234d3d97816d505a","mcp_get_code":{"code_sha256":"234d3d97816d505a"}},{"arxiv_id":"2410.24219","paper":"/paper/enhancing-motion-in-text-to-video-generation","title":"Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pr-ryan/demo","path":"utils/distributed.py","file_url":"https://github.com/pr-ryan/demo/blob/HEAD/utils/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d504697143a06b9","mcp_get_code":{"code_sha256":"1d504697143a06b9"}},{"arxiv_id":"2410.06912","paper":"/paper/compositional-entailment-learning-for","title":"Compositional Entailment Learning for Hyperbolic Vision-Language Models","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PalAvik/hycoclip","path":"hycoclip/models.py","file_url":"https://github.com/PalAvik/hycoclip/blob/HEAD/hycoclip/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"585179954e7e9e63","mcp_get_code":{"code_sha256":"585179954e7e9e63"}},{"arxiv_id":"2410.03658","paper":"/paper/raft-realistic-attacks-to-fool-text-detectors","title":"RAFT: Realistic Attacks to Fool Text Detectors","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jameslwang/raft","path":"detectors/baselines.py","file_url":"https://github.com/jameslwang/raft/blob/HEAD/detectors/baselines.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a5da194b02e3776","mcp_get_code":{"code_sha256":"2a5da194b02e3776"}},{"arxiv_id":"2406.02596","paper":"/paper/slow-and-steady-wins-the-race-maintaining","title":"Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dojeon-ai/hare-tortoise","path":"src/common/metrics.py","file_url":"https://github.com/dojeon-ai/hare-tortoise/blob/HEAD/src/common/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ed281db32072a644","mcp_get_code":{"code_sha256":"ed281db32072a644"}},{"arxiv_id":"2405.13954","paper":"/paper/what-is-your-data-worth-to-gpt-llm-scale-data","title":"What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions","date":"2024-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"logix-project/logix","path":"logix/utils.py","file_url":"https://github.com/logix-project/logix/blob/HEAD/logix/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d3e53200c9f0c7cb","mcp_get_code":{"code_sha256":"d3e53200c9f0c7cb"}},{"arxiv_id":"2405.06264","paper":"/paper/selective-focus-investigating-semantics","title":"Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PannenetsF/SelectiveFocus","path":"pad/utils/runners/lane_det_quant_trainer.py","file_url":"https://github.com/PannenetsF/SelectiveFocus/blob/HEAD/pad/utils/runners/lane_det_quant_trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"41ff89724cf234e1","mcp_get_code":{"code_sha256":"41ff89724cf234e1"}},{"arxiv_id":"2403.18913","paper":"/paper/unidepth-universal-monocular-metric-depth","title":"UniDepth: Universal Monocular Metric Depth Estimation","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lpiccinelli-eth/unidepth","path":"unidepth/models/unidepthv1/unidepthv1.py","file_url":"https://github.com/lpiccinelli-eth/unidepth/blob/HEAD/unidepth/models/unidepthv1/unidepthv1.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"21f13ea283351acc","mcp_get_code":{"code_sha256":"21f13ea283351acc"}},{"arxiv_id":"2403.12459","paper":"/paper/non-negative-contrastive-learning","title":"Non-negative Contrastive Learning","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pku-ml/non_neg","path":"solo/losses/nnclr.py","file_url":"https://github.com/pku-ml/non_neg/blob/HEAD/solo/losses/nnclr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8847999984146a64","mcp_get_code":{"code_sha256":"8847999984146a64"}},{"arxiv_id":"2403.10153","paper":"/paper/improving-medical-multi-modal-contrastive","title":"Improving Medical Multi-modal Contrastive Learning with Expert Annotations","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ykumards/eclip","path":"eclip/model/eclip_module.py","file_url":"https://github.com/ykumards/eclip/blob/HEAD/eclip/model/eclip_module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"c734e1676e254908","mcp_get_code":{"code_sha256":"c734e1676e254908"}},{"arxiv_id":"2401.05952","paper":"/paper/llm-as-a-coauthor-the-challenges-of-detecting","title":"LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongping-chen/mixset","path":"methods/metric_based.py","file_url":"https://github.com/dongping-chen/mixset/blob/HEAD/methods/metric_based.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ec539267c749a0cc","mcp_get_code":{"code_sha256":"ec539267c749a0cc"}},{"arxiv_id":"2312.09109","paper":"/paper/videolcm-video-latent-consistency-model","title":"VideoLCM: Video Latent Consistency Model","date":"2023-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ali-vilab/VGen","path":"utils/distributed.py","file_url":"https://github.com/ali-vilab/VGen/blob/HEAD/utils/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1d504697143a06b9","mcp_get_code":{"code_sha256":"1d504697143a06b9"}},{"arxiv_id":"2310.09449","paper":"/paper/pairwise-similarity-learning-is-simple-1","title":"Pairwise Similarity Learning is SimPLE","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ydwen/opensphere","path":"opensphere/module/head/simple.py","file_url":"https://github.com/ydwen/opensphere/blob/HEAD/opensphere/module/head/simple.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30b806a186fae4cd","mcp_get_code":{"code_sha256":"30b806a186fae4cd"}},{"arxiv_id":"2309.14888","paper":"/paper/nearest-neighbor-guidance-for-out-of-1","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","date":"2023-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingkang50/openood","path":"openood/postprocessors/nnguide_postprocessor.py","file_url":"https://github.com/jingkang50/openood/blob/HEAD/openood/postprocessors/nnguide_postprocessor.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f1cf6e198d885d2","mcp_get_code":{"code_sha256":"4f1cf6e198d885d2"}},{"arxiv_id":"2308.15419","paper":"/paper/characterizing-learning-curves-during","title":"Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability","date":"2023-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tylerachang/lm-learning-curves","path":"analysis/run_crossrun_similarity.py","file_url":"https://github.com/tylerachang/lm-learning-curves/blob/HEAD/analysis/run_crossrun_similarity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7ce496e8bd1b24d","mcp_get_code":{"code_sha256":"b7ce496e8bd1b24d"}},{"arxiv_id":"2307.11984","paper":"/paper/learning-vision-and-language-navigation-from","title":"Learning Vision-and-Language Navigation from YouTube Videos","date":"2023-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeremylinky/youtube-vln","path":"utils/distributed.py","file_url":"https://github.com/jeremylinky/youtube-vln/blob/HEAD/utils/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d57e6390318ac16","mcp_get_code":{"code_sha256":"8d57e6390318ac16"}},{"arxiv_id":"2307.09688","paper":"/paper/amazon-m2-a-multilingual-multi-locale","title":"Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation","date":"2023-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haitaomao/amazon-m2","path":"task1/finetune.py","file_url":"https://github.com/haitaomao/amazon-m2/blob/HEAD/task1/finetune.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5071428b12fb4234","mcp_get_code":{"code_sha256":"5071428b12fb4234"}},{"arxiv_id":"2306.02018","paper":"/paper/videocomposer-compositional-video-synthesis","title":"VideoComposer: Compositional Video Synthesis with Motion Controllability","date":"2023-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"damo-vilab/videocomposer","path":"artist/ops/distributed.py","file_url":"https://github.com/damo-vilab/videocomposer/blob/HEAD/artist/ops/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d504697143a06b9","mcp_get_code":{"code_sha256":"1d504697143a06b9"}},{"arxiv_id":"2305.18409","paper":"/paper/direction-oriented-multi-objective-learning","title":"Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms","date":"2023-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-opt-lab/sdmgrad","path":"experiments/mean_rank.py","file_url":"https://github.com/ml-opt-lab/sdmgrad/blob/HEAD/experiments/mean_rank.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"47019f3895d2e9d3","mcp_get_code":{"code_sha256":"47019f3895d2e9d3"}},{"arxiv_id":"2305.11846","paper":"/paper/any-to-any-generation-via-composable","title":"Any-to-Any Generation via Composable Diffusion","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/i-Code","path":"i-Code-V3/core/sync.py","file_url":"https://github.com/microsoft/i-Code/blob/HEAD/i-Code-V3/core/sync.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5804369ab36a8596","mcp_get_code":{"code_sha256":"5804369ab36a8596"}},{"arxiv_id":"2303.14865","paper":"/paper/revisiting-multimodal-representation-in","title":"Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuxiaochen1103/fdt","path":"prototype/model/clip_fdt.py","file_url":"https://github.com/yuxiaochen1103/fdt/blob/HEAD/prototype/model/clip_fdt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"82984f55ee21d9df","mcp_get_code":{"code_sha256":"82984f55ee21d9df"}},{"arxiv_id":"2303.14822","paper":"/paper/mgtbench-benchmarking-machine-generated-text","title":"MGTBench: Benchmarking Machine-Generated Text Detection","date":"2023-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trustairlab/mgtbench","path":"methods/metric_based.py","file_url":"https://github.com/trustairlab/mgtbench/blob/HEAD/methods/metric_based.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c7cd9182dadcefb","mcp_get_code":{"code_sha256":"0c7cd9182dadcefb"}},{"arxiv_id":"2211.01834","paper":"/paper/toward-unsupervised-outlier-model-selection","title":"Toward Unsupervised Outlier Model Selection","date":"2022-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yzhao062/elect","path":"utility.py","file_url":"https://github.com/yzhao062/elect/blob/HEAD/utility.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"3d1f18721729e636","mcp_get_code":{"code_sha256":"3d1f18721729e636"}},{"arxiv_id":"2209.01404","paper":"/paper/towards-accurate-binary-neural-networks-via","title":"Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies","date":"2022-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sense-GVT/BCDNet","path":"prototype/model/a_3.py","file_url":"https://github.com/Sense-GVT/BCDNet/blob/HEAD/prototype/model/a_3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"426074b9c7f8d0b4","mcp_get_code":{"code_sha256":"426074b9c7f8d0b4"}},{"arxiv_id":"2208.07652","paper":"/paper/corpusbrain-pre-train-a-generative-retrieval","title":"CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks","date":"2022-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ict-bigdatalab/corpusbrain","path":"kilt/eval_retrieval.py","file_url":"https://github.com/ict-bigdatalab/corpusbrain/blob/HEAD/kilt/eval_retrieval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6a9bcdcb79e942e6","mcp_get_code":{"code_sha256":"6a9bcdcb79e942e6"}},{"arxiv_id":"2205.12609","paper":"/paper/towards-more-realistic-generation-of","title":"Generating Information-Seeking Conversations from Unlabeled Documents","date":"2022-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/KILT","path":"kilt/eval_retrieval.py","file_url":"https://github.com/facebookresearch/KILT/blob/HEAD/kilt/eval_retrieval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"6a9bcdcb79e942e6","mcp_get_code":{"code_sha256":"6a9bcdcb79e942e6"}},{"arxiv_id":"2205.09542","paper":"/paper/domain-enhanced-arbitrary-image-style","title":"Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning","date":"2022-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyxelsa/cast_pytorch","path":"models/torch_utils.py","file_url":"https://github.com/zyxelsa/cast_pytorch/blob/HEAD/models/torch_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2e77a87b7e25bf88","mcp_get_code":{"code_sha256":"2e77a87b7e25bf88"}},{"arxiv_id":"2203.09343","paper":"/paper/cyborgs-contrastively-bootstrapping-object","title":"CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation","date":"2022-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"renwang435/CYBORGS","path":"main_cyborgs.py","file_url":"https://github.com/renwang435/CYBORGS/blob/HEAD/main_cyborgs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"804a43b2bb223775","mcp_get_code":{"code_sha256":"804a43b2bb223775"}},{"arxiv_id":"2108.09105","paper":"/paper/airbert-in-domain-pretraining-for-vision-and","title":"Airbert: In-domain Pretraining for Vision-and-Language Navigation","date":"2021-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airbert-vln/airbert","path":"utils/distributed.py","file_url":"https://github.com/airbert-vln/airbert/blob/HEAD/utils/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d57e6390318ac16","mcp_get_code":{"code_sha256":"8d57e6390318ac16"}},{"arxiv_id":"2107.06882","paper":"/paper/conservative-objective-models-for-effective","title":"Conservative Objective Models for Effective Offline Model-Based Optimization","date":"2021-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rail-berkeley/design-baselines","path":"design_baselines/coms_cleaned/trainers.py","file_url":"https://github.com/rail-berkeley/design-baselines/blob/HEAD/design_baselines/coms_cleaned/trainers.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"95e9b9ea7dfff908","mcp_get_code":{"code_sha256":"95e9b9ea7dfff908"}},{"arxiv_id":"2002.06914","paper":"/paper/interpretable-and-fair-comparison-of-link","title":"On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods","date":"2020-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mberr/rank-based-evaluation","path":"src/kgm/eval/common.py","file_url":"https://github.com/mberr/rank-based-evaluation/blob/HEAD/src/kgm/eval/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24c3f21daf7bffe9","mcp_get_code":{"code_sha256":"24c3f21daf7bffe9"}},{"arxiv_id":"2001.08943","paper":"/paper/active-learning-for-entity-alignment","title":"Active Learning for Entity Alignment","date":"2020-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mberr/ea-active-learning","path":"src/kgm/eval/common.py","file_url":"https://github.com/mberr/ea-active-learning/blob/HEAD/src/kgm/eval/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"24c3f21daf7bffe9","mcp_get_code":{"code_sha256":"24c3f21daf7bffe9"}},{"arxiv_id":"1904.00720","paper":"/paper/coacor-code-annotation-for-code-retrieval","title":"CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning","date":"2019-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LittleYUYU/CoaCor","path":"code/CodeRetrieval-Main/code/ensemble.py","file_url":"https://github.com/LittleYUYU/CoaCor/blob/HEAD/code/CodeRetrieval-Main/code/ensemble.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a9f21950fb827c6d","mcp_get_code":{"code_sha256":"a9f21950fb827c6d"}},{"arxiv_id":"1903.09760","paper":"/paper/photorealistic-style-transfer-via-wavelet","title":"Photorealistic Style Transfer via Wavelet Transforms","date":"2019-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leolle/StyleTransfer","path":"utils/core.py","file_url":"https://github.com/leolle/StyleTransfer/blob/HEAD/utils/core.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f416806a9d75fdf","mcp_get_code":{"code_sha256":"3f416806a9d75fdf"}},{"arxiv_id":"1805.08805","paper":"/paper/resource-aware-person-re-identification","title":"Resource Aware Person Re-identification across Multiple Resolutions","date":"2018-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/DARENet","path":"budgeted_stream/evaluation.py","file_url":"https://github.com/mileyan/DARENet/blob/HEAD/budgeted_stream/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63bb25a8b3b786aa","mcp_get_code":{"code_sha256":"63bb25a8b3b786aa"}},{"arxiv_id":"1802.06474","paper":"/paper/a-closed-form-solution-to-photorealistic","title":"A Closed-form Solution to Photorealistic Image Stylization","date":"2018-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/WCT2","path":"utils/core.py","file_url":"https://github.com/clovaai/WCT2/blob/HEAD/utils/core.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f416806a9d75fdf","mcp_get_code":{"code_sha256":"3f416806a9d75fdf"}},{"arxiv_id":"1503.05671","paper":"/paper/optimizing-neural-networks-with-kronecker","title":"Optimizing Neural Networks with Kronecker-factored Approximate Curvature","date":"2015-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gpauloski/kfac_pytorch","path":"kfac/base_preconditioner.py","file_url":"https://github.com/gpauloski/kfac_pytorch/blob/HEAD/kfac/base_preconditioner.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3278f8b9bebda5c0","mcp_get_code":{"code_sha256":"3278f8b9bebda5c0"}},{"arxiv_id":"Xu_Versatile_Diffusion_Text_Images_and_Variations_All_in_One_Diffusion_ICCV_2023_paper","paper":null,"title":"arXiv:Xu_Versatile_Diffusion_Text_Images_and_Variations_All_in_One_Diffusion_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SHI-Labs/Versatile-Diffusion","path":"lib/sync.py","file_url":"https://github.com/SHI-Labs/Versatile-Diffusion/blob/HEAD/lib/sync.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5804369ab36a8596","mcp_get_code":{"code_sha256":"5804369ab36a8596"}},{"arxiv_id":"2023.emnlp-main.616","paper":null,"title":"arXiv:2023.emnlp-main.616","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xinleihe/MGTBench","path":"methods/metric_based.py","file_url":"https://github.com/xinleihe/MGTBench/blob/HEAD/methods/metric_based.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c7cd9182dadcefb","mcp_get_code":{"code_sha256":"0c7cd9182dadcefb"}}]}