{"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-bound","entry":"activation_bound","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":6,"n_papers_ran":6,"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":1,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":0},"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":"2406.04724","paper":"/paper/probabilistic-perspectives-on-error","title":"Probabilistic Perspectives on Error Minimization in Adversarial Reinforcement Learning","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umd-huang-lab/WocaR-RL","path":"WocaR-DQN/SA-DQN/models.py","file_url":"https://github.com/umd-huang-lab/WocaR-RL/blob/HEAD/WocaR-DQN/SA-DQN/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}},{"arxiv_id":"2403.04050","paper":"/paper/belief-enriched-pessimistic-q-learning","title":"Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SliencerX/Belief-enriched-robust-Q-learning","path":"models_wocar.py","file_url":"https://github.com/SliencerX/Belief-enriched-robust-Q-learning/blob/HEAD/models_wocar.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}},{"arxiv_id":"2402.02165","paper":"/paper/towards-optimal-adversarial-robust-q-learning","title":"Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leoranlmia/CAR-DQN","path":"IBP/ibp.py","file_url":"https://github.com/leoranlmia/CAR-DQN/blob/HEAD/IBP/ibp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}},{"arxiv_id":"2111.06063","paper":"/paper/on-the-equivalence-between-neural-network-and","title":"On the Equivalence between Neural Network and Support Vector Machine","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leslie-CH/equiv-nn-svm","path":"ibp.py","file_url":"https://github.com/leslie-CH/equiv-nn-svm/blob/HEAD/ibp.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":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}},{"arxiv_id":"2008.01976","paper":"/paper/robust-deep-reinforcement-learning-through","title":"Robust Deep Reinforcement Learning through Adversarial Loss","date":"2020-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tuomaso/radial_rl","path":"A3C/ibp.py","file_url":"https://github.com/tuomaso/radial_rl/blob/HEAD/A3C/ibp.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":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}},{"arxiv_id":"1907.09470","paper":"/paper/characterizing-attacks-on-deep-reinforcement","title":"Characterizing Attacks on Deep Reinforcement Learning","date":"2019-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ssg-research/flare","path":"src/ibp.py","file_url":"https://github.com/ssg-research/flare/blob/HEAD/src/ibp.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":"69017b3a991e0815","mcp_get_code":{"code_sha256":"69017b3a991e0815"}}]}