{"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/prod","entry":"prod","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":40,"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":24,"n_samples_ran":7,"n_samples_fingerprinted":7,"n_places":41,"n_places_pointer_only":15,"by_status":{"ran_honours":0,"ran_violates":5,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":17},"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":"2602.18858","paper":"/paper/arxiv-2602-18858","title":"Hyperbolic Busemann Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"GitZH-Chen/HBNN","path":"lib/geoopt/utils.py","file_url":"https://github.com/GitZH-Chen/HBNN/blob/HEAD/lib/geoopt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4e7ce8697742d11","mcp_get_code":{"code_sha256":"d4e7ce8697742d11"}},{"arxiv_id":"2602.16897","paper":"/paper/arxiv-2602-16897","title":"Machine Learning Hamiltonians are Accurate Energy-Force Predictors","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"seongsukim-ml/QHFlow2","path":"src/utils.py","file_url":"https://github.com/seongsukim-ml/QHFlow2/blob/HEAD/src/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0aeca26969b9a56","mcp_get_code":{"code_sha256":"b0aeca26969b9a56"}},{"arxiv_id":"2601.19849","paper":"/paper/arxiv-2601-19849","title":"HexFormer: Hyperbolic Vision Transformer with Exponential Map Aggregation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"HayaAlyoussef/HexFormer","path":"lib/geoopt/utils.py","file_url":"https://github.com/HayaAlyoussef/HexFormer/blob/HEAD/lib/geoopt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4e7ce8697742d11","mcp_get_code":{"code_sha256":"d4e7ce8697742d11"}},{"arxiv_id":"2507.21648","paper":null,"title":"arXiv:2507.21648","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"rrkhan/HGE","path":"lib/geoopt/utils.py","file_url":"https://github.com/rrkhan/HGE/blob/HEAD/lib/geoopt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4e7ce8697742d11","mcp_get_code":{"code_sha256":"d4e7ce8697742d11"}},{"arxiv_id":"2502.01171","paper":"/paper/efficient-and-scalable-density-functional","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/sphnet","path":"src/models/SPHNet.py","file_url":"https://github.com/microsoft/sphnet/blob/HEAD/src/models/SPHNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0aeca26969b9a56","mcp_get_code":{"code_sha256":"b0aeca26969b9a56"}},{"arxiv_id":"2408.06277","paper":"/paper/multi-marginal-schrodinger-bridges-with","title":"Multi-marginal Schrödinger Bridges with Iterative Reference Refinement","date":"2024-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YunyiShen/SBIRR","path":"package/SBIRR/mem_utils.py","file_url":"https://github.com/YunyiShen/SBIRR/blob/HEAD/package/SBIRR/mem_utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"2406.03794","paper":"/paper/infusing-self-consistency-into-density","title":"Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zun-wang/deqhnet","path":"src/QHNet/models/DEQHNet.py","file_url":"https://github.com/zun-wang/deqhnet/blob/HEAD/src/QHNet/models/DEQHNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0aeca26969b9a56","mcp_get_code":{"code_sha256":"b0aeca26969b9a56"}},{"arxiv_id":"2406.01583","paper":"/paper/decomposing-and-interpreting-image","title":"Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SriramB-98/vit-decompose","path":"helpers/linear_decompose.py","file_url":"https://github.com/SriramB-98/vit-decompose/blob/HEAD/helpers/linear_decompose.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f008b9c99180b5f5","mcp_get_code":{"code_sha256":"f008b9c99180b5f5"}},{"arxiv_id":"2404.14047","paper":"/paper/how-good-are-low-bit-quantized-llama3-models","title":"An empirical study of LLaMA3 quantization: from LLMs to MLLMs","date":"2024-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"macaronlin/llama3-quantization","path":"irqlora.py","file_url":"https://github.com/macaronlin/llama3-quantization/blob/HEAD/irqlora.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"2402.05445","paper":"/paper/accurate-lora-finetuning-quantization-of-llms","title":"Accurate LoRA-Finetuning Quantization of LLMs via Information Retention","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"htqin/ir-qlora","path":"utils.py","file_url":"https://github.com/htqin/ir-qlora/blob/HEAD/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"2402.02456","paper":"/paper/discovering-more-effective-tensor-network","title":"tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs)","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChaoLiAtRIKEN/tngps","path":"tenmul4.py","file_url":"https://github.com/ChaoLiAtRIKEN/tngps/blob/HEAD/tenmul4.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"2311.03153","paper":"/paper/architectural-sweet-spots-for-modeling-human","title":"Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!","date":"2023-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phhei/relateperspectives-sweetspots","path":"argperspectives/models/RecommenderComponents.py","file_url":"https://github.com/phhei/relateperspectives-sweetspots/blob/HEAD/argperspectives/models/RecommenderComponents.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"19c35905c9854f5b","mcp_get_code":{"code_sha256":"19c35905c9854f5b"}},{"arxiv_id":"2310.19889","paper":"/paper/exploring-geometry-of-blind-spots-in-vision-1","title":"Exploring Geometry of Blind Spots in Vision Models","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sriramb-98/blindspots-neurips-sub","path":"level_set_traversal.py","file_url":"https://github.com/sriramb-98/blindspots-neurips-sub/blob/HEAD/level_set_traversal.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dce0c5ec0be00a6","mcp_get_code":{"code_sha256":"4dce0c5ec0be00a6"}},{"arxiv_id":"2306.17100","paper":"/paper/rl4co-an-extensive-reinforcement-learning-for","title":"RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/rl","path":"torchrl/_utils.py","file_url":"https://github.com/facebookresearch/rl/blob/HEAD/torchrl/_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"eb9333cd6b23461f","mcp_get_code":{"code_sha256":"eb9333cd6b23461f"}},{"arxiv_id":"2306.09549","paper":"/paper/qh9-a-quantum-hamiltonian-prediction-1","title":"QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"divelab/AIRS","path":"OpenDFT/QHBench/QH9/models/QHNet.py","file_url":"https://github.com/divelab/AIRS/blob/HEAD/OpenDFT/QHBench/QH9/models/QHNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"b0aeca26969b9a56","mcp_get_code":{"code_sha256":"b0aeca26969b9a56"}},{"arxiv_id":"2306.04922","paper":"/paper/efficient-and-equivariant-graph-networks-for","title":"Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"b0aeca26969b9a56","mcp_get_code":{"code_sha256":"b0aeca26969b9a56"}},{"arxiv_id":"2303.15919","paper":"/paper/hyperbolic-geometry-in-computer-vision-a","title":"Fully Hyperbolic Convolutional Neural Networks for Computer Vision","date":"2023-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kschwethelm/hyperboliccv","path":"code/lib/geoopt/utils.py","file_url":"https://github.com/kschwethelm/hyperboliccv/blob/HEAD/code/lib/geoopt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4e7ce8697742d11","mcp_get_code":{"code_sha256":"d4e7ce8697742d11"}},{"arxiv_id":"2302.04362","paper":"/paper/disentangling-learning-representations-with","title":"Disentangling Learning Representations with Density Estimation","date":"2023-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ericyeats/gcae-disentanglement","path":"src/disentanglement_utils.py","file_url":"https://github.com/ericyeats/gcae-disentanglement/blob/HEAD/src/disentanglement_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31238bf1de0249d3","mcp_get_code":{"code_sha256":"31238bf1de0249d3"}},{"arxiv_id":"2210.05144","paper":"/paper/mixture-of-attention-heads-selecting","title":"Mixture of Attention Heads: Selecting Attention Heads Per Token","date":"2022-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sovrasov/flops-counter.pytorch","path":"ptflops/aten_ops.py","file_url":"https://github.com/sovrasov/flops-counter.pytorch/blob/HEAD/ptflops/aten_ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"185b3f3ea726676d","mcp_get_code":{"code_sha256":"185b3f3ea726676d"}},{"arxiv_id":"2208.09021","paper":"/paper/vault-augmenting-the-vision-and-language","title":"VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Media","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gchochla/vault","path":"vault/train_utils.py","file_url":"https://github.com/gchochla/vault/blob/HEAD/vault/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5a5d3e4bf55e79d","mcp_get_code":{"code_sha256":"e5a5d3e4bf55e79d"}},{"arxiv_id":"2207.09572","paper":"/paper/towards-robust-multivariate-time-series","title":"Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms","date":"2022-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awslabs/gluonts","path":"src/gluonts/itertools.py","file_url":"https://github.com/awslabs/gluonts/blob/HEAD/src/gluonts/itertools.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":"67059eb6565db923","mcp_get_code":{"code_sha256":"67059eb6565db923"}},{"arxiv_id":"2207.09453","paper":"/paper/e3nn-euclidean-neural-networks","title":"e3nn: Euclidean Neural Networks","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atomicarchitects/phonax","path":"phonax/nequip_model.py","file_url":"https://github.com/atomicarchitects/phonax/blob/HEAD/phonax/nequip_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f10eb76dacbfed5","mcp_get_code":{"code_sha256":"3f10eb76dacbfed5"}},{"arxiv_id":"2203.11815","paper":"/paper/clustering-units-in-neural-networks-upstream","title":"Clustering units in neural networks: upstream vs downstream information","date":"2022-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wrongu/modularity","path":"models/cifar10.py","file_url":"https://github.com/wrongu/modularity/blob/HEAD/models/cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a828da824800b5de","mcp_get_code":{"code_sha256":"a828da824800b5de"}},{"arxiv_id":"2112.14436","paper":"/paper/monte-carlo-em-for-deep-time-series-anomaly","title":"Monte Carlo EM for Deep Time Series Anomaly Detection","date":"2021-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Francois-Aubet/gluon-ts","path":"src/gluonts/itertools.py","file_url":"https://github.com/Francois-Aubet/gluon-ts/blob/HEAD/src/gluonts/itertools.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":"b5c0811c003f958f","mcp_get_code":{"code_sha256":"b5c0811c003f958f"}},{"arxiv_id":"2108.00981","paper":"/paper/psa-gan-progressive-self-attention-gans-for","title":"PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series","date":"2021-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awslabs/gluon-ts","path":"src/gluonts/itertools.py","file_url":"https://github.com/awslabs/gluon-ts/blob/HEAD/src/gluonts/itertools.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":"67059eb6565db923","mcp_get_code":{"code_sha256":"67059eb6565db923"}},{"arxiv_id":"2106.02081","paper":"/paper/solving-schrodinger-bridges-via-maximum","title":"Solving Schrödinger Bridges via Maximum Likelihood","date":"2021-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"franciscovargas/GP_Sinkhorn","path":"gp_sinkhorn/mem_utils.py","file_url":"https://github.com/franciscovargas/GP_Sinkhorn/blob/HEAD/gp_sinkhorn/mem_utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"2105.14594","paper":"/paper/sparse-uncertainty-representation-in-deep-1","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","date":"2021-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/bayesianize","path":"bnn/nn/mixins/variational/inducing.py","file_url":"https://github.com/microsoft/bayesianize/blob/HEAD/bnn/nn/mixins/variational/inducing.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f1e71e2afa65ee4","mcp_get_code":{"code_sha256":"0f1e71e2afa65ee4"}},{"arxiv_id":"2004.06660","paper":"/paper/weight-poisoning-attacks-on-pre-trained","title":"Weight Poisoning Attacks on Pre-trained Models","date":"2020-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neulab/RIPPLe","path":"constrained_poison.py","file_url":"https://github.com/neulab/RIPPLe/blob/HEAD/constrained_poison.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":"99f4817ce6cc133b","mcp_get_code":{"code_sha256":"99f4817ce6cc133b"}},{"arxiv_id":"2002.10410","paper":"/paper/lagrangian-decomposition-for-neural-network","title":"Lagrangian Decomposition for Neural Network Verification","date":"2020-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oval-group/decomposition-plnn-bounds","path":"plnn_bounds/proxlp_solver/utils.py","file_url":"https://github.com/oval-group/decomposition-plnn-bounds/blob/HEAD/plnn_bounds/proxlp_solver/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1c49e5ae3f68bbc7","mcp_get_code":{"code_sha256":"1c49e5ae3f68bbc7"}},{"arxiv_id":"1911.10416","paper":"/paper/intermittent-demand-forecasting-with-deep","title":"Intermittent Demand Forecasting with Deep Renewal Processes","date":"2019-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manujosephv/deeprenewalprocess","path":"deeprenewal/deeprenewal/_network.py","file_url":"https://github.com/manujosephv/deeprenewalprocess/blob/HEAD/deeprenewal/deeprenewal/_network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0e3c7bfe8f66898","mcp_get_code":{"code_sha256":"f0e3c7bfe8f66898"}},{"arxiv_id":"1910.07065","paper":"/paper/reverse-derivative-categories","title":"Reverse derivative categories","date":null,"month_inferred_from_arxiv_id":"2019-10","title_source":"archive","repo":"statusfailed/catgrad","path":"catgrad/core/operation.py","file_url":"https://github.com/statusfailed/catgrad/blob/HEAD/catgrad/core/operation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ed818983163a2e5","mcp_get_code":{"code_sha256":"8ed818983163a2e5"}},{"arxiv_id":"1910.03002","paper":"/paper/high-dimensional-multivariate-forecasting","title":"High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes","date":"2019-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbohlkeschneider/gluon-ts","path":"src/gluonts/itertools.py","file_url":"https://github.com/mbohlkeschneider/gluon-ts/blob/HEAD/src/gluonts/itertools.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":"b5c0811c003f958f","mcp_get_code":{"code_sha256":"b5c0811c003f958f"}},{"arxiv_id":"1906.06818","paper":"/paper/stacked-capsule-autoencoders","title":"Stacked Capsule Autoencoders","date":"2019-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KohavTal/SCAE_Project","path":"torch_scae/general_utils.py","file_url":"https://github.com/KohavTal/SCAE_Project/blob/HEAD/torch_scae/general_utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"1906.06187","paper":"/paper/nlprolog-reasoning-with-weak-unification-for-1","title":"NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leonweber/nlprolog","path":"prolog.py","file_url":"https://github.com/leonweber/nlprolog/blob/HEAD/prolog.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f97356fe5f557abd","mcp_get_code":{"code_sha256":"f97356fe5f557abd"}},{"arxiv_id":"1906.05264","paper":"/paper/gluonts-probabilistic-time-series-models-in","title":"GluonTS: Probabilistic Time Series Models in Python","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elenaehrlich/gluon-ts","path":"src/gluonts/itertools.py","file_url":"https://github.com/elenaehrlich/gluon-ts/blob/HEAD/src/gluonts/itertools.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":"67059eb6565db923","mcp_get_code":{"code_sha256":"67059eb6565db923"}},{"arxiv_id":"1906.05264","paper":"/paper/gluonts-probabilistic-time-series-models-in","title":"GluonTS: Probabilistic Time Series Models in Python","date":"2019-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"canerturkmen/gluon-ts","path":"src/gluonts/itertools.py","file_url":"https://github.com/canerturkmen/gluon-ts/blob/HEAD/src/gluonts/itertools.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":"b5c0811c003f958f","mcp_get_code":{"code_sha256":"b5c0811c003f958f"}},{"arxiv_id":"1904.08489","paper":"/paper/semantic-adversarial-attacks-parametric","title":"Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ameya005/Semantic_Adversarial_Attacks","path":"simple_classifier.py","file_url":"https://github.com/ameya005/Semantic_Adversarial_Attacks/blob/HEAD/simple_classifier.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d5acfb0087d1ee7","mcp_get_code":{"code_sha256":"6d5acfb0087d1ee7"}},{"arxiv_id":"1810.12894","paper":"/paper/exploration-by-random-network-distillation","title":"Exploration by Random Network Distillation","date":"2018-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uoe-agents/derl","path":"derl/intrinsic_rewards/rnd/rnd.py","file_url":"https://github.com/uoe-agents/derl/blob/HEAD/derl/intrinsic_rewards/rnd/rnd.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fe739431c704a67","mcp_get_code":{"code_sha256":"2fe739431c704a67"}},{"arxiv_id":"1810.11530","paper":"/paper/automatic-differentiation-in-ml-where-we-are","title":"Automatic differentiation in ML: Where we are and where we should be going","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mila-udem/myia","path":"myia/public_api.py","file_url":"https://github.com/mila-udem/myia/blob/HEAD/myia/public_api.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d13c1f646dd643d6","mcp_get_code":{"code_sha256":"d13c1f646dd643d6"}},{"arxiv_id":"1711.05847","paper":"/paper/learning-deep-compositional-grammatical","title":"AOGNets: Compositional Grammatical Architectures for Deep Learning","date":"2017-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xilaili/AOGNet","path":"aognet/utils/memonger.py","file_url":"https://github.com/xilaili/AOGNet/blob/HEAD/aognet/utils/memonger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d4bf79898fff9b84","mcp_get_code":{"code_sha256":"d4bf79898fff9b84"}},{"arxiv_id":"aaai_29453","paper":null,"title":"arXiv:aaai_29453","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shapsider/scintegration","path":"integration_c/src/num.py","file_url":"https://github.com/shapsider/scintegration/blob/HEAD/integration_c/src/num.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8fb5257f5890e497","mcp_get_code":{"code_sha256":"8fb5257f5890e497"}}]}