{"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/relu-2","entry":"ReLU","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":8,"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":8,"n_samples_ran":4,"n_samples_fingerprinted":3,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":4},"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":"2410.10254","paper":"/paper/lolcats-on-low-rank-linearizing-of-large","title":"LoLCATs: On Low-Rank Linearizing of Large Language Models","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hazyresearch/lolcats","path":"src/model/linear_attention/linear_window_attention_sw_linear.py","file_url":"https://github.com/hazyresearch/lolcats/blob/HEAD/src/model/linear_attention/linear_window_attention_sw_linear.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f8a915d0998d5a10","mcp_get_code":{"code_sha256":"f8a915d0998d5a10"}},{"arxiv_id":"2401.09840","paper":"/paper/freed-improving-rl-agents-for-fragment-based","title":"FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airi-institute/ffreed","path":"ffreed/env/reward.py","file_url":"https://github.com/airi-institute/ffreed/blob/HEAD/ffreed/env/reward.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4542b8f7b6c43580","mcp_get_code":{"code_sha256":"4542b8f7b6c43580"}},{"arxiv_id":"2211.17244","paper":"/paper/overcoming-the-convex-relaxation-barrier-for","title":"Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations","date":"2022-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/CROWN-Robustness-Certification","path":"get_bounds_ours.py","file_url":"https://github.com/IBM/CROWN-Robustness-Certification/blob/HEAD/get_bounds_ours.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":"753617a588e4fc41","mcp_get_code":{"code_sha256":"753617a588e4fc41"}},{"arxiv_id":"2211.05520","paper":"/paper/unravelling-the-performance-of-physics","title":"Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems","date":"2022-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"M3RG-IITD/benchmarking_graph","path":"src/models.py","file_url":"https://github.com/M3RG-IITD/benchmarking_graph/blob/HEAD/src/models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b3c8460a326d3611","mcp_get_code":{"code_sha256":"b3c8460a326d3611"}},{"arxiv_id":"2209.11588","paper":"/paper/learning-rigid-body-dynamics-with-lagrangian","title":"Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network","date":"2022-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"m3rg-iitd/rigid_body_dynamics_graph","path":"src/models.py","file_url":"https://github.com/m3rg-iitd/rigid_body_dynamics_graph/blob/HEAD/src/models.py","status":"unverified","verification_level":0,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86a60d7eb39f2e3d","mcp_get_code":{"code_sha256":"86a60d7eb39f2e3d"}},{"arxiv_id":"1907.12353","paper":"/paper/recursive-cascaded-networks-for-unsupervised","title":"Recursive Cascaded Networks for Unsupervised Medical Image Registration","date":"2019-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"w19787/Recursive-Cascaded-Networks-TF2.0","path":"network/utils.py","file_url":"https://github.com/w19787/Recursive-Cascaded-Networks-TF2.0/blob/HEAD/network/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"d3012985effa9042","mcp_get_code":{"code_sha256":"d3012985effa9042"}},{"arxiv_id":"1811.00866","paper":"/paper/efficient-neural-network-robustness","title":"Efficient Neural Network Robustness Certification with General Activation Functions","date":"2018-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanzhang12/CertifiedReLURobustness","path":"get_bounds_ours.py","file_url":"https://github.com/huanzhang12/CertifiedReLURobustness/blob/HEAD/get_bounds_ours.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"753617a588e4fc41","mcp_get_code":{"code_sha256":"753617a588e4fc41"}},{"arxiv_id":"1811.00866","paper":"/paper/efficient-neural-network-robustness","title":"Efficient Neural Network Robustness Certification with General Activation Functions","date":"2018-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanzhang12/RecurJac-Jacobian-Bounds","path":"bound_base.py","file_url":"https://github.com/huanzhang12/RecurJac-Jacobian-Bounds/blob/HEAD/bound_base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"67421da32aae154e","mcp_get_code":{"code_sha256":"67421da32aae154e"}},{"arxiv_id":"1511.06434","paper":"/paper/unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adityabingi/DCGAN-TF2.0","path":"dcgan.py","file_url":"https://github.com/adityabingi/DCGAN-TF2.0/blob/HEAD/dcgan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3ff534de9715e1d","mcp_get_code":{"code_sha256":"b3ff534de9715e1d"}}]}