{"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/build","entry":"build","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":31,"n_papers_ran":4,"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":35,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":37,"n_places_pointer_only":11,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":31},"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":"2609.15657","paper":"/paper/arxiv-2609-15657","title":"Predictive Likelihood Ratios for Language Model Watermark Detection","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"MaStatLab/PLRWatermark","path":"code/build_prompts.py","file_url":"https://github.com/MaStatLab/PLRWatermark/blob/HEAD/code/build_prompts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d1069cecaec3698","mcp_get_code":{"code_sha256":"3d1069cecaec3698"}},{"arxiv_id":"2608.24189","paper":"/paper/arxiv-2608-24189","title":"MemUse: Moving Memory Evaluation from Direct QA to Natural Integration in Long-Term Human-AI Conversation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"ryuichi-sumida/memuse","path":"baselines/run_fullcontext.py","file_url":"https://github.com/ryuichi-sumida/memuse/blob/HEAD/baselines/run_fullcontext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ad773531f0f3af7","mcp_get_code":{"code_sha256":"7ad773531f0f3af7"}},{"arxiv_id":"2607.22722","paper":"/paper/arxiv-2607-22722","title":"A New Kind of Adversarial Example: Measuring the Human-Model Gap, and Its Relationship to OOD Detection","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"alikayyam/NKE_attack","path":"nke/models.py","file_url":"https://github.com/alikayyam/NKE_attack/blob/HEAD/nke/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"641c44fd42c8bef1","mcp_get_code":{"code_sha256":"641c44fd42c8bef1"}},{"arxiv_id":"2607.17765","paper":"/paper/arxiv-2607-17765","title":"FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"graphuofm/FIFA2026LLM","path":"src/build_dataset.py","file_url":"https://github.com/graphuofm/FIFA2026LLM/blob/HEAD/src/build_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a627f040f13a9194","mcp_get_code":{"code_sha256":"a627f040f13a9194"}},{"arxiv_id":"2606.28344","paper":"/paper/arxiv-2606-28344","title":"PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"StarTrail-org/PixelRAG","path":"index/src/pixelrag_index/pipelines.py","file_url":"https://github.com/StarTrail-org/PixelRAG/blob/HEAD/index/src/pixelrag_index/pipelines.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":"90cb9e46d89c0b72","mcp_get_code":{"code_sha256":"90cb9e46d89c0b72"}},{"arxiv_id":"2606.00329","paper":"/paper/arxiv-2606-00329","title":"Benchmarking Recursive-Collapse Warning Claims Under Matched False-Positive Control","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"davidmullett/loopzero-paper-public","path":"src/loopzero_paper/benchmarks/recommender/build_real_covariates.py","file_url":"https://github.com/davidmullett/loopzero-paper-public/blob/HEAD/src/loopzero_paper/benchmarks/recommender/build_real_covariates.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":"9f9b9987533928bb","mcp_get_code":{"code_sha256":"9f9b9987533928bb"}},{"arxiv_id":"2605.31500","paper":"/paper/arxiv-2605-31500","title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yandex-research/On-Efficient-Scaling-Of-GNNs","path":"src/models/registry.py","file_url":"https://github.com/yandex-research/On-Efficient-Scaling-Of-GNNs/blob/HEAD/src/models/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"b484b587dc7496d6","mcp_get_code":{"code_sha256":"b484b587dc7496d6"}},{"arxiv_id":"2604.24720","paper":"/paper/arxiv-2604-24720","title":"Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ikii-sd/pba2026-crazyrichteam","path":"src/models/model_baseline.py","file_url":"https://github.com/ikii-sd/pba2026-crazyrichteam/blob/HEAD/src/models/model_baseline.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"862c10c06cda19eb","mcp_get_code":{"code_sha256":"862c10c06cda19eb"}},{"arxiv_id":"2604.24720","paper":"/paper/arxiv-2604-24720","title":"Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ikii-sd/pba2026-crazyrichteam","path":"src/models/model_improved.py","file_url":"https://github.com/ikii-sd/pba2026-crazyrichteam/blob/HEAD/src/models/model_improved.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e1c1d75d4a820c2","mcp_get_code":{"code_sha256":"6e1c1d75d4a820c2"}},{"arxiv_id":"2604.24720","paper":"/paper/arxiv-2604-24720","title":"Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ikii-sd/pba2026-crazyrichteam","path":"src/models/model_large.py","file_url":"https://github.com/ikii-sd/pba2026-crazyrichteam/blob/HEAD/src/models/model_large.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91c5a0e971609b59","mcp_get_code":{"code_sha256":"91c5a0e971609b59"}},{"arxiv_id":"2602.15189","paper":"/paper/arxiv-2602-15189","title":"ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"ScrapeGraphAI/scrapegraph-100k-paper","path":"modelling/prompts.py","file_url":"https://github.com/ScrapeGraphAI/scrapegraph-100k-paper/blob/HEAD/modelling/prompts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e4642fbe4e9a07a4","mcp_get_code":{"code_sha256":"e4642fbe4e9a07a4"}},{"arxiv_id":"2507.05108","paper":"/paper/reviving-cultural-heritage-a-novel-approach","title":"Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration","date":"2025-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SCUT-DLVCLab/AutoHDR","path":"models/upocr.py","file_url":"https://github.com/SCUT-DLVCLab/AutoHDR/blob/HEAD/models/upocr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ff59d2987b57e4ba","mcp_get_code":{"code_sha256":"ff59d2987b57e4ba"}},{"arxiv_id":"2410.18926","paper":"/paper/lorann-low-rank-matrix-factorization-for","title":"LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ejaasaari/lorann-experiments","path":"install.py","file_url":"https://github.com/ejaasaari/lorann-experiments/blob/HEAD/install.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a2ffd7252fad4b2","mcp_get_code":{"code_sha256":"1a2ffd7252fad4b2"}},{"arxiv_id":"2409.17424","paper":"/paper/results-of-the-big-ann-neurips-23-competition","title":"Results of the Big ANN: NeurIPS'23 competition","date":"2024-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"harsha-simhadri/big-ann-benchmarks","path":"install.py","file_url":"https://github.com/harsha-simhadri/big-ann-benchmarks/blob/HEAD/install.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4a00404229a68dcc","mcp_get_code":{"code_sha256":"4a00404229a68dcc"}},{"arxiv_id":"2404.09387","paper":"/paper/rankclip-ranking-consistent-language-image","title":"RankCLIP: Ranking-Consistent Language-Image Pretraining","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jam1ezhang/rankclip","path":"pkgs/openai/model.py","file_url":"https://github.com/jam1ezhang/rankclip/blob/HEAD/pkgs/openai/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"27b459903dd9ce81","mcp_get_code":{"code_sha256":"27b459903dd9ce81"}},{"arxiv_id":"2310.05862","paper":"/paper/better-safe-than-sorry-pre-training-clip","title":"Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigml-cs-ucla/safeclip","path":"SafeCLIP/pkgs/openai/model.py","file_url":"https://github.com/bigml-cs-ucla/safeclip/blob/HEAD/SafeCLIP/pkgs/openai/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"27b459903dd9ce81","mcp_get_code":{"code_sha256":"27b459903dd9ce81"}},{"arxiv_id":"2308.07061","paper":"/paper/machine-unlearning-solutions-and-challenges","title":"Machine Unlearning: Solutions and Challenges","date":"2023-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tamlhp/awesome-machine-unlearning","path":"algorithms/UnfoldedSelf-ReconstructionHashing/install.py","file_url":"https://github.com/tamlhp/awesome-machine-unlearning/blob/HEAD/algorithms/UnfoldedSelf-ReconstructionHashing/install.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"daeac69bbd11aadf","mcp_get_code":{"code_sha256":"daeac69bbd11aadf"}},{"arxiv_id":"2307.15700","paper":"/paper/memotr-long-term-memory-augmented-transformer","title":"MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking","date":"2023-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcg-nju/memotr","path":"models/deformable_transformer.py","file_url":"https://github.com/mcg-nju/memotr/blob/HEAD/models/deformable_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8302d68a2f073468","mcp_get_code":{"code_sha256":"8302d68a2f073468"}},{"arxiv_id":"2210.07442","paper":"/paper/frame-mining-a-free-lunch-for-learning","title":"Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds","date":"2022-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuanlinli17/corl_22_frame_mining","path":"pyrl/pyrl/networks/builder.py","file_url":"https://github.com/xuanlinli17/corl_22_frame_mining/blob/HEAD/pyrl/pyrl/networks/builder.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":"053001fc2285043b","mcp_get_code":{"code_sha256":"053001fc2285043b"}},{"arxiv_id":"2209.12213","paper":"/paper/eco-tr-efficient-correspondences-finding-via","title":"ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement","date":"2022-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dltan7/ECO-TR","path":"src/models/ecotr_model.py","file_url":"https://github.com/dltan7/ECO-TR/blob/HEAD/src/models/ecotr_model.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":"983f2c32d9f35628","mcp_get_code":{"code_sha256":"983f2c32d9f35628"}},{"arxiv_id":"2208.08965","paper":"/paper/gsrformer-grounded-situation-recognition","title":"GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiqic/gsrformer","path":"datasets/swig.py","file_url":"https://github.com/zhiqic/gsrformer/blob/HEAD/datasets/swig.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"e335e7e2c2f17628","mcp_get_code":{"code_sha256":"e335e7e2c2f17628"}},{"arxiv_id":"2203.10897","paper":"/paper/unified-multivariate-gaussian-mixture-for","title":"Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaosu-zhu/McQuic","path":"mcquic/modules/builder.py","file_url":"https://github.com/xiaosu-zhu/McQuic/blob/HEAD/mcquic/modules/builder.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":"d789e18c19c20aa0","mcp_get_code":{"code_sha256":"d789e18c19c20aa0"}},{"arxiv_id":"2111.10135","paper":"/paper/grounded-situation-recognition-with","title":"Grounded Situation Recognition with Transformers","date":"2021-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhcho99/gsrtr","path":"datasets/swig.py","file_url":"https://github.com/jhcho99/gsrtr/blob/HEAD/datasets/swig.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"edfd696fbd294e13","mcp_get_code":{"code_sha256":"edfd696fbd294e13"}},{"arxiv_id":"2107.14483","paper":"/paper/maniskill-learning-from-demonstrations","title":"ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations","date":"2021-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haosulab/ManiSkill-Learn","path":"mani_skill_learn/networks/builder.py","file_url":"https://github.com/haosulab/ManiSkill-Learn/blob/HEAD/mani_skill_learn/networks/builder.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":"67976edf323e2f17","mcp_get_code":{"code_sha256":"67976edf323e2f17"}},{"arxiv_id":"2105.12085","paper":"/paper/dsanet-dynamic-segment-aggregation-network","title":"DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning","date":"2021-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whwu95/DSANet","path":"codes/models/builder.py","file_url":"https://github.com/whwu95/DSANet/blob/HEAD/codes/models/builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"67976edf323e2f17","mcp_get_code":{"code_sha256":"67976edf323e2f17"}},{"arxiv_id":"2102.06177","paper":"/paper/multi-task-reinforcement-learning-with","title":"Multi-Task Reinforcement Learning with Context-based Representations","date":"2021-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/mtenv","path":"mtenv/envs/hipbmdp/env.py","file_url":"https://github.com/facebookresearch/mtenv/blob/HEAD/mtenv/envs/hipbmdp/env.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e05ca3c4484a295","mcp_get_code":{"code_sha256":"0e05ca3c4484a295"}},{"arxiv_id":"2005.14310","paper":"/paper/predicting-goal-directed-human-attention","title":"Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-stonybrook/Scanpath_Prediction","path":"irl_dcb/builder.py","file_url":"https://github.com/cvlab-stonybrook/Scanpath_Prediction/blob/HEAD/irl_dcb/builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06e89ab5539e3aae","mcp_get_code":{"code_sha256":"06e89ab5539e3aae"}},{"arxiv_id":"2003.00482","paper":"/paper/state-aware-tracker-for-real-time-video","title":"State-Aware Tracker for Real-Time Video Object Segmentation","date":"2020-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MegviiDetection/video_analyst","path":"videoanalyst/model/builder.py","file_url":"https://github.com/MegviiDetection/video_analyst/blob/HEAD/videoanalyst/model/builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e127c82cc613f1d9","mcp_get_code":{"code_sha256":"e127c82cc613f1d9"}},{"arxiv_id":"1907.06038","paper":"/paper/m3d-rpn-monocular-3d-region-proposal-network","title":"M3D-RPN: Monocular 3D Region Proposal Network for Object Detection","date":"2019-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garrickbrazil/M3D-RPN","path":"models/densenet121_3d_dilate.py","file_url":"https://github.com/garrickbrazil/M3D-RPN/blob/HEAD/models/densenet121_3d_dilate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1c47eb3c9a5da30c","mcp_get_code":{"code_sha256":"1c47eb3c9a5da30c"}},{"arxiv_id":"1907.06038","paper":"/paper/m3d-rpn-monocular-3d-region-proposal-network","title":"M3D-RPN: Monocular 3D Region Proposal Network for Object Detection","date":"2019-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garrickbrazil/M3D-RPN","path":"models/densenet121_3d_dilate_depth_aware.py","file_url":"https://github.com/garrickbrazil/M3D-RPN/blob/HEAD/models/densenet121_3d_dilate_depth_aware.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e3da10ee5eae517","mcp_get_code":{"code_sha256":"2e3da10ee5eae517"}},{"arxiv_id":"1903.11027","paper":"/paper/nuscenes-a-multimodal-dataset-for-autonomous","title":"nuScenes: A multimodal dataset for autonomous driving","date":"2019-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nutonomy/second.pytorch","path":"second/pytorch/builder/optimizer_builder.py","file_url":"https://github.com/nutonomy/second.pytorch/blob/HEAD/second/pytorch/builder/optimizer_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1fcec29c9194340f","mcp_get_code":{"code_sha256":"1fcec29c9194340f"}},{"arxiv_id":"1805.00237","paper":"/paper/randomly-weighted-cnns-for-music-audio","title":"Randomly weighted CNNs for (music) audio classification","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"jordipons/elmarc","path":"src/dl_models.py","file_url":"https://github.com/jordipons/elmarc/blob/HEAD/src/dl_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09f096ee2270341e","mcp_get_code":{"code_sha256":"09f096ee2270341e"}},{"arxiv_id":"aaai_28245","paper":null,"title":"arXiv:aaai_28245","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shannanyinxiang/ViTEraser","path":"models/viteraser.py","file_url":"https://github.com/shannanyinxiang/ViTEraser/blob/HEAD/models/viteraser.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"997f2a10b5a97651","mcp_get_code":{"code_sha256":"997f2a10b5a97651"}},{"arxiv_id":"aaai_28245","paper":null,"title":"arXiv:aaai_28245","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shannanyinxiang/ViTEraser","path":"models/segmim.py","file_url":"https://github.com/shannanyinxiang/ViTEraser/blob/HEAD/models/segmim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db550ace28175eaa","mcp_get_code":{"code_sha256":"db550ace28175eaa"}},{"arxiv_id":"aaai_28245","paper":null,"title":"arXiv:aaai_28245","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shannanyinxiang/ViTEraser","path":"datasets/segmim.py","file_url":"https://github.com/shannanyinxiang/ViTEraser/blob/HEAD/datasets/segmim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"601365b6fc104e7f","mcp_get_code":{"code_sha256":"601365b6fc104e7f"}},{"arxiv_id":"aaai_28245","paper":null,"title":"arXiv:aaai_28245","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shannanyinxiang/ViTEraser","path":"datasets/texterase.py","file_url":"https://github.com/shannanyinxiang/ViTEraser/blob/HEAD/datasets/texterase.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e72d80ae297be4d","mcp_get_code":{"code_sha256":"0e72d80ae297be4d"}},{"arxiv_id":"Zhang_Spiking_Transformers_for_Event-Based_Single_Object_Tracking_CVPR_2022_paper","paper":null,"title":"arXiv:Zhang_Spiking_Transformers_for_Event-Based_Single_Object_Tracking_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Jee-King/CVPR2022_STNet","path":"videoanalyst/model/builder.py","file_url":"https://github.com/Jee-King/CVPR2022_STNet/blob/HEAD/videoanalyst/model/builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"48acb618c8672361","mcp_get_code":{"code_sha256":"48acb618c8672361"}}]}