{"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/activation-layer","entry":"activation_layer","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":34,"n_papers_ran":24,"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":14,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":35,"n_places_pointer_only":10,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":3,"unverified":7},"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":"2608.09605","paper":"/paper/arxiv-2608-09605","title":"TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"WNQzhu/TSPORec","path":"code/RQVAE/models/layers.py","file_url":"https://github.com/WNQzhu/TSPORec/blob/HEAD/code/RQVAE/models/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc089588049a4d8c","mcp_get_code":{"code_sha256":"fc089588049a4d8c"}},{"arxiv_id":"2605.27374","paper":"/paper/arxiv-2605-27374","title":"ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"westlake-repl/PixelRec","path":"code/REC/model/layers.py","file_url":"https://github.com/westlake-repl/PixelRec/blob/HEAD/code/REC/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2604.16379","paper":"/paper/arxiv-2604-16379","title":"LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"RUCAIBox/RecBole","path":"recbole/model/layers.py","file_url":"https://github.com/RUCAIBox/RecBole/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2604.05379","paper":"/paper/arxiv-2604-05379","title":"Retrieve-then-Adapt: Test-Time Retrieval-Augmentation for Sequential Recommendation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"xingt-tang/ReAd","path":"recbole/model/layers.py","file_url":"https://github.com/xingt-tang/ReAd/blob/HEAD/recbole/model/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd7349fade1cc74e","mcp_get_code":{"code_sha256":"dd7349fade1cc74e"}},{"arxiv_id":"2603.11462","paper":"/paper/arxiv-2603-11462","title":"Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"AONE-NLP/NEXTPP","path":"tpp/model/baselayer.py","file_url":"https://github.com/AONE-NLP/NEXTPP/blob/HEAD/tpp/model/baselayer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d49d5a4ade4321b5","mcp_get_code":{"code_sha256":"d49d5a4ade4321b5"}},{"arxiv_id":"2509.03131","paper":"/paper/arxiv-2509-03131","title":"RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"reczoo/RecBase","path":"models/clvae.py","file_url":"https://github.com/reczoo/RecBase/blob/HEAD/models/clvae.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"827516ae99ad061f","mcp_get_code":{"code_sha256":"827516ae99ad061f"}},{"arxiv_id":"2505.10940","paper":"/paper/2505-10940","title":"Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation","date":"2025-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Code2Q/TagCF","path":"recbole/model/layers.py","file_url":"https://github.com/Code2Q/TagCF/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2411.01537","paper":"/paper/linrec-linear-attention-mechanism-for-long","title":"LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Applied-Machine-Learning-Lab/LinRec","path":"layers.py","file_url":"https://github.com/Applied-Machine-Learning-Lab/LinRec/blob/HEAD/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2407.19239","paper":"/paper/matrrec-uniting-mamba-and-transformer-for","title":"MaTrRec: Uniting Mamba and Transformer for Sequential Recommendation","date":"2024-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unintelligentmumu/matrrec","path":"recbole/model/layers.py","file_url":"https://github.com/unintelligentmumu/matrrec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2406.01034","paper":"/paper/fourierkan-gcf-fourier-kolmogorov-arnold","title":"FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinfeng-xu/fkan-gcf","path":"models/common/layers.py","file_url":"https://github.com/jinfeng-xu/fkan-gcf/blob/HEAD/models/common/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2405.00986","paper":"/paper/multi-intent-aware-session-based","title":"Multi-intent-aware Session-based Recommendation","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jin530/miasrec","path":"recbole/model/layers.py","file_url":"https://github.com/jin530/miasrec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2404.18465","paper":"/paper/m3oe-multi-domain-multi-task-mixture-of","title":"M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"applied-machine-learning-lab/m3oe","path":"MDMTRec/basic/activation.py","file_url":"https://github.com/applied-machine-learning-lab/m3oe/blob/HEAD/MDMTRec/basic/activation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30e596864fce6bbc","mcp_get_code":{"code_sha256":"30e596864fce6bbc"}},{"arxiv_id":"2402.11523","paper":"/paper/neighborhood-enhanced-supervised-contrastive","title":"Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeiJieSun/NESCL","path":"recbole/model/layers.py","file_url":"https://github.com/PeiJieSun/NESCL/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2401.15635","paper":"/paper/recdcl-dual-contrastive-learning-for","title":"RecDCL: Dual Contrastive Learning for Recommendation","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/recdcl","path":"recbole/model/layers.py","file_url":"https://github.com/thudm/recdcl/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2401.00797","paper":"/paper/distillation-is-all-you-need-for-practically","title":"Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential Recommendation","date":"2024-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rucaibox/ckd-mdsr","path":"recbole/model/layers.py","file_url":"https://github.com/rucaibox/ckd-mdsr/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2309.06789","paper":"/paper/an-image-dataset-for-benchmarking-recommender","title":"An Image Dataset for Benchmarking Recommender Systems with Raw Pixels","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"westlake-repl/pixelrec","path":"code/REC/model/layers.py","file_url":"https://github.com/westlake-repl/pixelrec/blob/HEAD/code/REC/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2308.09419","paper":"/paper/attention-calibration-for-transformer-based","title":"Attention Calibration for Transformer-based Sequential Recommendation","date":"2023-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aim-se/ac-tsr","path":"recbole/model/layers.py","file_url":"https://github.com/aim-se/ac-tsr/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2307.08097","paper":"/paper/easytpp-towards-open-benchmarking-the","title":"EasyTPP: Towards Open Benchmarking Temporal Point Processes","date":"2023-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/easytemporalpointprocess","path":"easy_tpp/model/baselayer.py","file_url":"https://github.com/ant-research/easytemporalpointprocess/blob/HEAD/easy_tpp/model/baselayer.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":"d49d5a4ade4321b5","mcp_get_code":{"code_sha256":"d49d5a4ade4321b5"}},{"arxiv_id":"2305.16646","paper":"/paper/language-models-can-improve-event-prediction-1","title":"Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning","date":"2023-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/EasyTemporalPointProcess","path":"easy_tpp/model/baselayer.py","file_url":"https://github.com/ant-research/EasyTemporalPointProcess/blob/HEAD/easy_tpp/model/baselayer.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":"d49d5a4ade4321b5","mcp_get_code":{"code_sha256":"d49d5a4ade4321b5"}},{"arxiv_id":"2304.09184","paper":"/paper/frequency-enhanced-hybrid-attention-network","title":"Frequency Enhanced Hybrid Attention Network for Sequential Recommendation","date":"2023-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sudaada/fearec","path":"recbole/model/layers.py","file_url":"https://github.com/sudaada/fearec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2304.07763","paper":"/paper/meta-optimized-contrastive-learning-for","title":"Meta-optimized Contrastive Learning for Sequential Recommendation","date":"2023-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qinhsiu/mclrec","path":"recbole/model/layers.py","file_url":"https://github.com/qinhsiu/mclrec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2303.11780","paper":"/paper/debiased-contrastive-learning-for-sequential","title":"Debiased Contrastive Learning for Sequential Recommendation","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkuds/dcrec","path":"recbole/model/layers.py","file_url":"https://github.com/hkuds/dcrec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2206.12811","paper":"/paper/towards-representation-alignment-and","title":"Towards Representation Alignment and Uniformity in Collaborative Filtering","date":"2022-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuwangcy/directau","path":"recbole/model/layers.py","file_url":"https://github.com/thuwangcy/directau/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2205.08776","paper":"/paper/adamct-adaptive-mixture-of-cnn-transformer","title":"AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation","date":"2022-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juyongjiang/adamct","path":"recbole/model/layers.py","file_url":"https://github.com/juyongjiang/adamct/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2204.11046","paper":"/paper/decoupled-side-information-fusion-for","title":"Decoupled Side Information Fusion for Sequential Recommendation","date":"2022-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AIM-SE/DIF-SR","path":"recbole/model/layers.py","file_url":"https://github.com/AIM-SE/DIF-SR/blob/HEAD/recbole/model/layers.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":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2110.05730","paper":"/paper/contrastive-learning-for-representation","title":"Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation","date":"2021-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RuihongQiu/DuoRec","path":"recbole/model/layers.py","file_url":"https://github.com/RuihongQiu/DuoRec/blob/HEAD/recbole/model/layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5b0bb176f34b7a4","mcp_get_code":{"code_sha256":"b5b0bb176f34b7a4"}},{"arxiv_id":"2008.13535","paper":"/paper/dcn-m-improved-deep-cross-network-for-feature","title":"DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems","date":"2020-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shenweichen/DeepCTR-Torch","path":"deepctr_torch/layers/activation.py","file_url":"https://github.com/shenweichen/DeepCTR-Torch/blob/HEAD/deepctr_torch/layers/activation.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":"e8b1b2ea4e2ca71d","mcp_get_code":{"code_sha256":"e8b1b2ea4e2ca71d"}},{"arxiv_id":"1905.09433","paper":"/paper/fibinet-combining-feature-importance-and","title":"FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"datawhalechina/torch-rechub","path":"torch_rechub/basic/activation.py","file_url":"https://github.com/datawhalechina/torch-rechub/blob/HEAD/torch_rechub/basic/activation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9cfbe7def435449","mcp_get_code":{"code_sha256":"b9cfbe7def435449"}},{"arxiv_id":"1905.06482","paper":"/paper/deep-session-interest-network-for-click","title":"Deep Session Interest Network for Click-Through Rate Prediction","date":"2019-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shenweichen/DeepCTR-PyTorch","path":"deepctr_torch/layers/activation.py","file_url":"https://github.com/shenweichen/DeepCTR-PyTorch/blob/HEAD/deepctr_torch/layers/activation.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":"e8b1b2ea4e2ca71d","mcp_get_code":{"code_sha256":"e8b1b2ea4e2ca71d"}},{"arxiv_id":"1902.00293","paper":"/paper/end-to-end-lane-detection-through","title":"End-to-end Lane Detection through Differentiable Least-Squares Fitting","date":"2019-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wvangansbeke/LaneDetection_End2End","path":"Backprojection_Loss/Networks/LSQ_layer.py","file_url":"https://github.com/wvangansbeke/LaneDetection_End2End/blob/HEAD/Backprojection_Loss/Networks/LSQ_layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6530ce91d9113378","mcp_get_code":{"code_sha256":"6530ce91d9113378"}},{"arxiv_id":"1812.09764","paper":"/paper/neural-persistence-a-complexity-measure-for","title":"Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology","date":"2018-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BorgwardtLab/Neural-Persistence","path":"src/model_definitions.py","file_url":"https://github.com/BorgwardtLab/Neural-Persistence/blob/HEAD/src/model_definitions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"917ed6b40d46eadd","mcp_get_code":{"code_sha256":"917ed6b40d46eadd"}},{"arxiv_id":"1809.03672","paper":"/paper/deep-interest-evolution-network-for-click","title":"Deep Interest Evolution Network for Click-Through Rate Prediction","date":"2018-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kupuSs/DIEN-pipline","path":"main/activation.py","file_url":"https://github.com/kupuSs/DIEN-pipline/blob/HEAD/main/activation.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":"e104b06ecdb1248e","mcp_get_code":{"code_sha256":"e104b06ecdb1248e"}},{"arxiv_id":"1511.07289","paper":"/paper/fast-and-accurate-deep-network-learning-by","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","date":"2015-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MalayAgarwal-Lee/MesoNet-DeepFakeDetection","path":"mesonet/model.py","file_url":"https://github.com/MalayAgarwal-Lee/MesoNet-DeepFakeDetection/blob/HEAD/mesonet/model.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":"5770a3f04d128ada","mcp_get_code":{"code_sha256":"5770a3f04d128ada"}},{"arxiv_id":"1511.07289","paper":"/paper/fast-and-accurate-deep-network-learning-by","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","date":"2015-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MalayAgarwal-Lee/SteeringWheelAnglePredictor","path":"model.py","file_url":"https://github.com/MalayAgarwal-Lee/SteeringWheelAnglePredictor/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"153c0f814833ed63","mcp_get_code":{"code_sha256":"153c0f814833ed63"}},{"arxiv_id":"aaai_26347","paper":null,"title":"arXiv:aaai_26347","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bytedance/LargeBatchCTR","path":"deepctr/layers/activation.py","file_url":"https://github.com/bytedance/LargeBatchCTR/blob/HEAD/deepctr/layers/activation.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":"b425425199081002","mcp_get_code":{"code_sha256":"b425425199081002"}}]}