{"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/pearson-correlation","entry":"pearson_correlation","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":15,"n_papers_ran":10,"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":12,"n_samples_ran":7,"n_samples_fingerprinted":5,"n_places":16,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":2,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"unverified":5},"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":"2511.17914","paper":"/paper/arxiv-2511-17914","title":"Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"j-cyoung/ADSA_DD","path":"EDC/cifar10/train/direct_train_adsa.py","file_url":"https://github.com/j-cyoung/ADSA_DD/blob/HEAD/EDC/cifar10/train/direct_train_adsa.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"593d10016514986d","mcp_get_code":{"code_sha256":"593d10016514986d"}},{"arxiv_id":"2509.23871","paper":"/paper/arxiv-2509-23871","title":"Taught Well Learned Ill: Towards Distillation-conditional Backdoor Attack","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"WhitolfChen/SCAR","path":"core/dist_kd.py","file_url":"https://github.com/WhitolfChen/SCAR/blob/HEAD/core/dist_kd.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8eb964c14770de5c","mcp_get_code":{"code_sha256":"8eb964c14770de5c"}},{"arxiv_id":"2503.18731","paper":"/paper/thermalizer-stable-autoregressive-neural","title":"Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos","date":"2025-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pdearena/pdearena","path":"pdearena/modules/loss.py","file_url":"https://github.com/pdearena/pdearena/blob/HEAD/pdearena/modules/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"d83235be62a2525b","mcp_get_code":{"code_sha256":"d83235be62a2525b"}},{"arxiv_id":"2412.13074","paper":"/paper/predicting-change-not-states-an-alternate","title":"Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates","date":"2024-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anthonyzhou-1/temporal_pdes","path":"common/loss.py","file_url":"https://github.com/anthonyzhou-1/temporal_pdes/blob/HEAD/common/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5ac79cc5c3d8f7fa","mcp_get_code":{"code_sha256":"5ac79cc5c3d8f7fa"}},{"arxiv_id":"2410.11758","paper":"/paper/latent-action-pretraining-from-videos","title":"Latent Action Pretraining from Videos","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simpler-env/SimplerEnv","path":"simpler_env/utils/metrics.py","file_url":"https://github.com/simpler-env/SimplerEnv/blob/HEAD/simpler_env/utils/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9dbd294d9713950c","mcp_get_code":{"code_sha256":"9dbd294d9713950c"}},{"arxiv_id":"2406.01512","paper":"/paper/mad-multi-alignment-meg-to-text-decoding","title":"MAD: Multi-Alignment MEG-to-Text Decoding","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuspeech/mad-meg2text","path":"utils/loss.py","file_url":"https://github.com/neuspeech/mad-meg2text/blob/HEAD/utils/loss.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d00eea76cc523c64","mcp_get_code":{"code_sha256":"d00eea76cc523c64"}},{"arxiv_id":"2405.14124","paper":"/paper/mixture-of-experts-meets-prompt-based","title":"Mixture of Experts Meets Prompt-Based Continual Learning","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Minhchuyentoancbn/MoE_PromptCL","path":"engines/loss.py","file_url":"https://github.com/Minhchuyentoancbn/MoE_PromptCL/blob/HEAD/engines/loss.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8eb964c14770de5c","mcp_get_code":{"code_sha256":"8eb964c14770de5c"}},{"arxiv_id":"2402.08561","paper":"/paper/data-efficiency-and-long-term-prediction","title":"Data efficiency and long term prediction capabilities for neural operator surrogate models of core and edge plasma codes","date":null,"month_inferred_from_arxiv_id":"2024-02","title_source":"archive","repo":"microsoft/pdearena","path":"pdearena/modules/loss.py","file_url":"https://github.com/microsoft/pdearena/blob/HEAD/pdearena/modules/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d83235be62a2525b","mcp_get_code":{"code_sha256":"d83235be62a2525b"}},{"arxiv_id":"2402.01830","paper":"/paper/peer-review-in-llms-automatic-evaluation","title":"PiCO: Peer Review in LLMs based on the Consistency Optimization","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PKU-YuanGroup/Peer-review-in-LLMs","path":"con_optimization/main_ablation.py","file_url":"https://github.com/PKU-YuanGroup/Peer-review-in-LLMs/blob/HEAD/con_optimization/main_ablation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70b8da050907a87c","mcp_get_code":{"code_sha256":"70b8da050907a87c"}},{"arxiv_id":"2402.01140","paper":"/paper/root-cause-analysis-in-microservice-using","title":"Root Cause Analysis In Microservice Using Neural Granger Causal Discovery","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zmlin1998/run","path":"models/utils.py","file_url":"https://github.com/zmlin1998/run/blob/HEAD/models/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a2bb0bd2911a8eca","mcp_get_code":{"code_sha256":"a2bb0bd2911a8eca"}},{"arxiv_id":"2311.17327","paper":"/paper/improving-self-supervised-molecular-1","title":"Improving Self-supervised Molecular Representation Learning using Persistent Homology","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LUOyk1999/Molecular-homology","path":"finetune/finetune_PI.py","file_url":"https://github.com/LUOyk1999/Molecular-homology/blob/HEAD/finetune/finetune_PI.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f2033d144dea2f7d","mcp_get_code":{"code_sha256":"f2033d144dea2f7d"}},{"arxiv_id":"2305.15032","paper":"/paper/how-to-distill-your-bert-an-empirical-study","title":"How to Distill your BERT: An Empirical Study on the Impact of Weight Initialisation and Distillation Objectives","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mainlp/How-to-distill-your-BERT","path":"task_agnostic_distillation/methods/pear_loss.py","file_url":"https://github.com/mainlp/How-to-distill-your-BERT/blob/HEAD/task_agnostic_distillation/methods/pear_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"78c3c61943df0cc0","mcp_get_code":{"code_sha256":"78c3c61943df0cc0"}},{"arxiv_id":"2205.10536","paper":"/paper/knowledge-distillation-from-a-stronger","title":"Knowledge Distillation from A Stronger Teacher","date":"2022-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hunto/dist_kd","path":"segmentation/losses/dist_kd.py","file_url":"https://github.com/hunto/dist_kd/blob/HEAD/segmentation/losses/dist_kd.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d00eea76cc523c64","mcp_get_code":{"code_sha256":"d00eea76cc523c64"}},{"arxiv_id":"2205.10536","paper":"/paper/knowledge-distillation-from-a-stronger","title":"Knowledge Distillation from A Stronger Teacher","date":"2022-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hunto/image_classification_sota","path":"lib/models/losses/dist_kd.py","file_url":"https://github.com/hunto/image_classification_sota/blob/HEAD/lib/models/losses/dist_kd.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8eb964c14770de5c","mcp_get_code":{"code_sha256":"8eb964c14770de5c"}},{"arxiv_id":"2204.02557","paper":"/paper/mixformer-mixing-features-across-windows-and","title":"MixFormer: Mixing Features across Windows and Dimensions","date":"2022-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flytocc/PaddleClas","path":"ppcls/loss/dist_loss.py","file_url":"https://github.com/flytocc/PaddleClas/blob/HEAD/ppcls/loss/dist_loss.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":"a7997903cea7558c","mcp_get_code":{"code_sha256":"a7997903cea7558c"}},{"arxiv_id":"2006.07882","paper":"/paper/uncovering-the-topology-of-time-varying-fmri","title":"Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence","date":"2020-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BorgwardtLab/fMRI_Cubical_Persistence","path":"ephemeral/predict_age.py","file_url":"https://github.com/BorgwardtLab/fMRI_Cubical_Persistence/blob/HEAD/ephemeral/predict_age.py","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"e89075f985ad3076","mcp_get_code":{"code_sha256":"e89075f985ad3076"}}]}