{"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/squash","entry":"squash","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":30,"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":30,"n_samples_ran":10,"n_samples_fingerprinted":9,"n_places":46,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":9,"ran_fixture":0,"ran":1,"unverified":20},"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":"2408.16944","paper":"/paper/flowretrieval-flow-guided-data-retrieval-for","title":"FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lihenglin/bridge_training_code","path":"data_processing/bridgedata_raw_to_numpy.py","file_url":"https://github.com/lihenglin/bridge_training_code/blob/HEAD/data_processing/bridgedata_raw_to_numpy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b78c78cefa5c620","mcp_get_code":{"code_sha256":"2b78c78cefa5c620"}},{"arxiv_id":"2312.15339","paper":"/paper/madi-learning-to-mask-distractions-for","title":"MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning","date":"2023-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bramgrooten/mask-distractions","path":"src/algorithms/modules.py","file_url":"https://github.com/bramgrooten/mask-distractions/blob/HEAD/src/algorithms/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"121cb336a1e1278a","mcp_get_code":{"code_sha256":"121cb336a1e1278a"}},{"arxiv_id":"2311.04245","paper":"/paper/gpt-st-generative-pre-training-of-spatio","title":"GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUDS/GPT-ST","path":"model/Pretrain_model/GPTST.py","file_url":"https://github.com/HKUDS/GPT-ST/blob/HEAD/model/Pretrain_model/GPTST.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c0581450b12d51cb","mcp_get_code":{"code_sha256":"c0581450b12d51cb"}},{"arxiv_id":"2311.04245","paper":"/paper/gpt-st-generative-pre-training-of-spatio","title":"GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"89d981e0b618190e","mcp_get_code":{"code_sha256":"89d981e0b618190e"}},{"arxiv_id":"2308.12952","paper":"/paper/bridgedata-v2-a-dataset-for-robot-learning-at","title":"BridgeData V2: A Dataset for Robot Learning at Scale","date":"2023-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rail-berkeley/BridgeData-V2","path":"data_processing/bridgedata_raw_to_numpy.py","file_url":"https://github.com/rail-berkeley/BridgeData-V2/blob/HEAD/data_processing/bridgedata_raw_to_numpy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b78c78cefa5c620","mcp_get_code":{"code_sha256":"2b78c78cefa5c620"}},{"arxiv_id":"2210.07241","paper":"/paper/visual-reinforcement-learning-with-self","title":"Visual Reinforcement Learning with Self-Supervised 3D Representations","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YanjieZe/rl3d","path":"src/algorithms/modules.py","file_url":"https://github.com/YanjieZe/rl3d/blob/HEAD/src/algorithms/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"121cb336a1e1278a","mcp_get_code":{"code_sha256":"121cb336a1e1278a"}},{"arxiv_id":"2206.05266","paper":"/paper/does-self-supervised-learning-really-improve","title":"Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?","date":"2022-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LostXine/elo-sac","path":"elo_sac.py","file_url":"https://github.com/LostXine/elo-sac/blob/HEAD/elo_sac.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2203.12759","paper":"/paper/asynchronous-reinforcement-learning-for-real","title":"Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yufengyuan/ur5_async_rl","path":"models.py","file_url":"https://github.com/yufengyuan/ur5_async_rl/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2201.07788","paper":"/paper/condor-self-supervised-canonicalization-of-3d","title":"ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes","date":"2022-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brown-ivl/ConDor","path":"ConDor/network_utils/activations.py","file_url":"https://github.com/brown-ivl/ConDor/blob/HEAD/ConDor/network_utils/activations.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ee731af0229a9ba","mcp_get_code":{"code_sha256":"6ee731af0229a9ba"}},{"arxiv_id":"2112.03939","paper":"/paper/photometric-redshifts-from-sdss-images-with","title":"Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network","date":null,"month_inferred_from_arxiv_id":"2021-12","title_source":"archive","repo":"biprateep/encapZulate-1","path":"src/encapzulate/base/capsulelayers.py","file_url":"https://github.com/biprateep/encapZulate-1/blob/HEAD/src/encapzulate/base/capsulelayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae823ae127199b54","mcp_get_code":{"code_sha256":"ae823ae127199b54"}},{"arxiv_id":"2112.03939","paper":"/paper/photometric-redshifts-from-sdss-images-with","title":"Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network","date":null,"month_inferred_from_arxiv_id":"2021-12","title_source":"archive","repo":"biprateep/encapZulate-1","path":"src/encapzulate/base/deepCapsLayers.py","file_url":"https://github.com/biprateep/encapZulate-1/blob/HEAD/src/encapzulate/base/deepCapsLayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c3da8b78efbfce9","mcp_get_code":{"code_sha256":"7c3da8b78efbfce9"}},{"arxiv_id":"2107.00644","paper":"/paper/stabilizing-deep-q-learning-with-convnets-and","title":"Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicklashansen/dmcontrol-generalization-benchmark","path":"src/algorithms/modules.py","file_url":"https://github.com/nicklashansen/dmcontrol-generalization-benchmark/blob/HEAD/src/algorithms/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"121cb336a1e1278a","mcp_get_code":{"code_sha256":"121cb336a1e1278a"}},{"arxiv_id":"2107.00644","paper":"/paper/stabilizing-deep-q-learning-with-convnets-and","title":"Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nicklashansen/svea-vit","path":"src/algorithms/modules.py","file_url":"https://github.com/nicklashansen/svea-vit/blob/HEAD/src/algorithms/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adedb876e87c2947","mcp_get_code":{"code_sha256":"adedb876e87c2947"}},{"arxiv_id":"2102.13268","paper":"/paper/robust-deep-reinforcement-learning-via-multi","title":"DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck","date":"2021-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bu-depend-lab/dribo","path":"DRIBO/DRIBO_sac.py","file_url":"https://github.com/bu-depend-lab/dribo/blob/HEAD/DRIBO/DRIBO_sac.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2010.10392","paper":"/paper/characterbert-reconciling-elmo-and-bert-for","title":"CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters","date":"2020-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IMPLabUniPr/UniParma-at-semeval-2021-task-5","path":"modeling/layer_ae.py","file_url":"https://github.com/IMPLabUniPr/UniParma-at-semeval-2021-task-5/blob/HEAD/modeling/layer_ae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5abcd8c6705f2368","mcp_get_code":{"code_sha256":"5abcd8c6705f2368"}},{"arxiv_id":"2010.09163","paper":"/paper/d2rl-deep-dense-architectures-in-1","title":"D2RL: Deep Dense Architectures in Reinforcement Learning","date":"2020-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pairlab/d2rl","path":"curl/curl_sac.py","file_url":"https://github.com/pairlab/d2rl/blob/HEAD/curl/curl_sac.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2010.07470","paper":"/paper/masked-contrastive-representation-learning","title":"Masked Contrastive Representation Learning for Reinforcement Learning","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"teslacool/m-curl","path":"curl_sac.py","file_url":"https://github.com/teslacool/m-curl/blob/HEAD/curl_sac.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2009.04278","paper":"/paper/dynode-neural-ordinary-differential-equations","title":"DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control","date":"2020-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vmartinezalvarez/DyNODE","path":"networks.py","file_url":"https://github.com/vmartinezalvarez/DyNODE/blob/HEAD/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc3a4c7a6f7a5ad4","mcp_get_code":{"code_sha256":"dc3a4c7a6f7a5ad4"}},{"arxiv_id":"2008.08476","paper":"/paper/nascaps-a-framework-for-neural-architecture","title":"NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks","date":"2020-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ehw-fit/nascaps","path":"layers/CapsLayer.py","file_url":"https://github.com/ehw-fit/nascaps/blob/HEAD/layers/CapsLayer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"521985ebd5591503","mcp_get_code":{"code_sha256":"521985ebd5591503"}},{"arxiv_id":"2008.08476","paper":"/paper/nascaps-a-framework-for-neural-architecture","title":"NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks","date":"2020-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ehw-fit/nascaps","path":"layers/CapsuleLayers.py","file_url":"https://github.com/ehw-fit/nascaps/blob/HEAD/layers/CapsuleLayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be9c101ac2975d5c","mcp_get_code":{"code_sha256":"be9c101ac2975d5c"}},{"arxiv_id":"2007.04309","paper":"/paper/self-supervised-policy-adaptation-during","title":"Self-Supervised Policy Adaptation during Deployment","date":"2020-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"2004.04136","paper":"/paper/curl-contrastive-unsupervised-representations","title":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning","date":"2020-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MishaLaskin/curl","path":"curl_sac.py","file_url":"https://github.com/MishaLaskin/curl/blob/HEAD/curl_sac.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"1911.03903","paper":"/paper/a-re-evaluation-of-knowledge-graph-completion","title":"A Re-evaluation of Knowledge Graph Completion Methods","date":"2019-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"svjan5/kg-reeval","path":"CapsE/capsuleLayer.py","file_url":"https://github.com/svjan5/kg-reeval/blob/HEAD/CapsE/capsuleLayer.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":"ad05325f767d6062","mcp_get_code":{"code_sha256":"ad05325f767d6062"}},{"arxiv_id":"1910.01741","paper":"/paper/improving-sample-efficiency-in-model-free-1","title":"Improving Sample Efficiency in Model-Free Reinforcement Learning from Images","date":"2019-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"denisyarats/pytorch_sac_ae","path":"sac_ae.py","file_url":"https://github.com/denisyarats/pytorch_sac_ae/blob/HEAD/sac_ae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4f442d1770eacd1","mcp_get_code":{"code_sha256":"e4f442d1770eacd1"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HopefulRational/DeepCaps-PyTorch","path":"deepcaps.py","file_url":"https://github.com/HopefulRational/DeepCaps-PyTorch/blob/HEAD/deepcaps.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ac5c90dc72f57f2","mcp_get_code":{"code_sha256":"4ac5c90dc72f57f2"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mfarhadi98/Use-Capsule-Networks-for-kddcup","path":"capslayers.py","file_url":"https://github.com/mfarhadi98/Use-Capsule-Networks-for-kddcup/blob/HEAD/capslayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c3da8b78efbfce9","mcp_get_code":{"code_sha256":"7c3da8b78efbfce9"}},{"arxiv_id":"1904.09546","paper":"/paper/deepcaps-going-deeper-with-capsule-networks","title":"DeepCaps: Going Deeper with Capsule Networks","date":"2019-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ugenteraan/Deep-CapsNet","path":"model.py","file_url":"https://github.com/Ugenteraan/Deep-CapsNet/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d3b03a5a3ef29c68","mcp_get_code":{"code_sha256":"d3b03a5a3ef29c68"}},{"arxiv_id":"1904.08900","paper":"/paper/190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Arno3165229/CornerNet_Traffic_Light","path":"capsulelayers.py","file_url":"https://github.com/Arno3165229/CornerNet_Traffic_Light/blob/HEAD/capsulelayers.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":"14c8e8b4bfa1bf08","mcp_get_code":{"code_sha256":"14c8e8b4bfa1bf08"}},{"arxiv_id":"1902.03368","paper":"/paper/skin-lesion-analysis-toward-melanoma-1","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","date":"2019-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Woodman718/FixCaps","path":"Module/model410.py","file_url":"https://github.com/Woodman718/FixCaps/blob/HEAD/Module/model410.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4eecff210748e3b","mcp_get_code":{"code_sha256":"b4eecff210748e3b"}},{"arxiv_id":"1808.04122","paper":"/paper/a-capsule-network-based-embedding-model-for-1","title":"A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization","date":"2018-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daiquocnguyen/CapsE","path":"capsuleLayer.py","file_url":"https://github.com/daiquocnguyen/CapsE/blob/HEAD/capsuleLayer.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":"521985ebd5591503","mcp_get_code":{"code_sha256":"521985ebd5591503"}},{"arxiv_id":"1808.03749","paper":"/paper/neural-network-encapsulation","title":"Neural Network Encapsulation","date":"2018-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hli2020/nn_capsulation","path":"layers/cap_layer.py","file_url":"https://github.com/hli2020/nn_capsulation/blob/HEAD/layers/cap_layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac7650b0121cd6c9","mcp_get_code":{"code_sha256":"ac7650b0121cd6c9"}},{"arxiv_id":"1807.10108","paper":"/paper/effects-of-degradations-on-deep-neural","title":"Effects of Degradations on Deep Neural Network Architectures","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prasunroy/cnn-on-degraded-images","path":"libs/CapsuleNetwork.py","file_url":"https://github.com/prasunroy/cnn-on-degraded-images/blob/HEAD/libs/CapsuleNetwork.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6051faed718b2c87","mcp_get_code":{"code_sha256":"6051faed718b2c87"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"e8e375ca46107111","mcp_get_code":{"code_sha256":"e8e375ca46107111"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ecstayalive/capsule_nn","path":"functional.py","file_url":"https://github.com/ecstayalive/capsule_nn/blob/HEAD/functional.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"43a28458e2019a51","mcp_get_code":{"code_sha256":"43a28458e2019a51"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hli2020/nn_encapsulation","path":"layers/cap_layer.py","file_url":"https://github.com/hli2020/nn_encapsulation/blob/HEAD/layers/cap_layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac7650b0121cd6c9","mcp_get_code":{"code_sha256":"ac7650b0121cd6c9"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danielhavir/capsule-network","path":"capsules.py","file_url":"https://github.com/danielhavir/capsule-network/blob/HEAD/capsules.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":"36782ecd204f05d4","mcp_get_code":{"code_sha256":"36782ecd204f05d4"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Riroaki/CapsNet","path":"capsnet.py","file_url":"https://github.com/Riroaki/CapsNet/blob/HEAD/capsnet.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":"89d981e0b618190e","mcp_get_code":{"code_sha256":"89d981e0b618190e"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adambielski/capsnet-pytorch","path":"net.py","file_url":"https://github.com/adambielski/capsnet-pytorch/blob/HEAD/net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5abcd8c6705f2368","mcp_get_code":{"code_sha256":"5abcd8c6705f2368"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Egesabanci/capsuleNetworks","path":"src/capsule_networks.py","file_url":"https://github.com/Egesabanci/capsuleNetworks/blob/HEAD/src/capsule_networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fed6154c45168494","mcp_get_code":{"code_sha256":"fed6154c45168494"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XifengGuo/CapsNet-Keras","path":"capsulelayers.py","file_url":"https://github.com/XifengGuo/CapsNet-Keras/blob/HEAD/capsulelayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc53226908b5d193","mcp_get_code":{"code_sha256":"bc53226908b5d193"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ameliajimenez/capsule-networks-medical-data-challenges","path":"networks/capsnet.py","file_url":"https://github.com/ameliajimenez/capsule-networks-medical-data-challenges/blob/HEAD/networks/capsnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42ce6a693b03e2f2","mcp_get_code":{"code_sha256":"42ce6a693b03e2f2"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanleet/CapsNet","path":"tools.py","file_url":"https://github.com/ethanleet/CapsNet/blob/HEAD/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6c4c09caf295822","mcp_get_code":{"code_sha256":"e6c4c09caf295822"}},{"arxiv_id":"1710.09829","paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lauromoraes/capsnet-promoter","path":"capsulelayers.py","file_url":"https://github.com/lauromoraes/capsnet-promoter/blob/HEAD/capsulelayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d1e743564e5d4d8c","mcp_get_code":{"code_sha256":"d1e743564e5d4d8c"}},{"arxiv_id":"1406.2661","paper":"/paper/generative-adversarial-networks","title":"Generative Adversarial Networks","date":"2014-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DanielLongo/CapsGAN","path":"CapsDiscriminator.py","file_url":"https://github.com/DanielLongo/CapsGAN/blob/HEAD/CapsDiscriminator.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8e375ca46107111","mcp_get_code":{"code_sha256":"e8e375ca46107111"}},{"arxiv_id":"aaai_29726","paper":null,"title":"arXiv:aaai_29726","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shesshan/CEIB","path":"model_utils.py","file_url":"https://github.com/shesshan/CEIB/blob/HEAD/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90f82cd330bf4777","mcp_get_code":{"code_sha256":"90f82cd330bf4777"}},{"arxiv_id":"aaai_17268","paper":null,"title":"arXiv:aaai_17268","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"uta-smile/HGCN","path":"layers.py","file_url":"https://github.com/uta-smile/HGCN/blob/HEAD/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ece1eb8ba21cc8ff","mcp_get_code":{"code_sha256":"ece1eb8ba21cc8ff"}}]}