{"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/stem","entry":"stem","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":11,"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":15,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":17,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":0,"unverified":13},"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":"2602.21910","paper":"/paper/arxiv-2602-21910","title":"The Error of Deep Operator Networks Is the Sum of Its Parts: Branch-Trunk and Mode Error Decompositions","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"jotaraz/ModeDecomposition-DeepONets","path":"make_seed_sweep.py","file_url":"https://github.com/jotaraz/ModeDecomposition-DeepONets/blob/HEAD/make_seed_sweep.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ca271fabe499bd1","mcp_get_code":{"code_sha256":"1ca271fabe499bd1"}},{"arxiv_id":"2408.03703","paper":"/paper/cas-vit-convolutional-additive-self-attention","title":"CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfang-zhang/cas-vit","path":"classification/model/rcvit.py","file_url":"https://github.com/tianfang-zhang/cas-vit/blob/HEAD/classification/model/rcvit.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b72ba06382340c08","mcp_get_code":{"code_sha256":"b72ba06382340c08"}},{"arxiv_id":"2212.08059","paper":"/paper/rethinking-vision-transformers-for-mobilenet","title":"Rethinking Vision Transformers for MobileNet Size and Speed","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"560e3160f50add18","mcp_get_code":{"code_sha256":"560e3160f50add18"}},{"arxiv_id":"2206.01191","paper":"/paper/efficientformer-vision-transformers-at","title":"EfficientFormer: Vision Transformers at MobileNet Speed","date":"2022-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-research/efficientformer","path":"models/efficientformer.py","file_url":"https://github.com/snap-research/efficientformer/blob/HEAD/models/efficientformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b72ba06382340c08","mcp_get_code":{"code_sha256":"b72ba06382340c08"}},{"arxiv_id":"2203.10581","paper":"/paper/cluster-tune-boost-cold-start-performance-in","title":"Cluster & Tune: Boost Cold Start Performance in Text Classification","date":"2022-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/intermediate-training-using-clustering","path":"run_experiment.py","file_url":"https://github.com/ibm/intermediate-training-using-clustering/blob/HEAD/run_experiment.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":"ac51901ff69f7646","mcp_get_code":{"code_sha256":"ac51901ff69f7646"}},{"arxiv_id":"2202.07800","paper":"/paper/not-all-patches-are-what-you-need-expediting","title":"Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations","date":"2022-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Retinal-Research/EVIT-UNET","path":"unet/eff_unet.py","file_url":"https://github.com/Retinal-Research/EVIT-UNET/blob/HEAD/unet/eff_unet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"560e3160f50add18","mcp_get_code":{"code_sha256":"560e3160f50add18"}},{"arxiv_id":"2112.02447","paper":"/paper/next-day-wildfire-spread-a-machine-learning","title":"Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data","date":"2021-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satellitevu/satellitevu-aws-disaster-response-hackathon","path":"deep_learning/model_resunet.py","file_url":"https://github.com/satellitevu/satellitevu-aws-disaster-response-hackathon/blob/HEAD/deep_learning/model_resunet.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":"48305b591734eef2","mcp_get_code":{"code_sha256":"48305b591734eef2"}},{"arxiv_id":"2104.13963","paper":"/paper/semi-supervised-learning-of-visual-features","title":"Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples","date":"2021-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sayakpaul/PAWS-TF","path":"models/resnet20.py","file_url":"https://github.com/sayakpaul/PAWS-TF/blob/HEAD/models/resnet20.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":"4771975a4479f016","mcp_get_code":{"code_sha256":"4771975a4479f016"}},{"arxiv_id":"2001.06588","paper":"/paper/flexibo-cost-aware-multi-objective","title":"FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks","date":"2020-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"softsys4ai/FlexiBO","path":"networks/resnet50.py","file_url":"https://github.com/softsys4ai/FlexiBO/blob/HEAD/networks/resnet50.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6bde0b827d9c7e67","mcp_get_code":{"code_sha256":"6bde0b827d9c7e67"}},{"arxiv_id":"2001.06588","paper":"/paper/flexibo-cost-aware-multi-objective","title":"FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks","date":"2020-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"softsys4ai/FlexiBO","path":"networks/squeezenet.py","file_url":"https://github.com/softsys4ai/FlexiBO/blob/HEAD/networks/squeezenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af56cd5321ea5452","mcp_get_code":{"code_sha256":"af56cd5321ea5452"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_1DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab72eeba5f2a8e01","mcp_get_code":{"code_sha256":"ab72eeba5f2a8e01"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_2DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ac04442aabc2a42","mcp_get_code":{"code_sha256":"0ac04442aabc2a42"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_v2_1DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_v2_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ef98d5a06da35e4","mcp_get_code":{"code_sha256":"7ef98d5a06da35e4"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/ResNet_v2_2DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/ResNet_v2_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e01a98528a53c09","mcp_get_code":{"code_sha256":"3e01a98528a53c09"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/SE_ResNet_1DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/SE_ResNet_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"57b2ad7b14fe19fe","mcp_get_code":{"code_sha256":"57b2ad7b14fe19fe"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/1DResNet-Builder-KERAS","path":"Codes/SE_ResNet_2DCNN.py","file_url":"https://github.com/Sakib1263/1DResNet-Builder-KERAS/blob/HEAD/Codes/SE_ResNet_2DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ba22ee1d1ad73ef","mcp_get_code":{"code_sha256":"1ba22ee1d1ad73ef"}},{"arxiv_id":"1608.06993","paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sakib1263/DenseNet-1D-2D-Tensorflow-Keras","path":"Codes/DenseNet_1DCNN.py","file_url":"https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras/blob/HEAD/Codes/DenseNet_1DCNN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"050e4107164ed015","mcp_get_code":{"code_sha256":"050e4107164ed015"}}]}