{"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/elementwise-logsumexp","entry":"elementwise_logsumexp","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":5,"n_papers_ran":5,"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":1,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"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":"2111.01026","paper":"/paper/introspective-distillation-for-robust","title":"Introspective Distillation for Robust Question Answering","date":"2021-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuleiniu/introd","path":"css/vqa_debias_loss_functions.py","file_url":"https://github.com/yuleiniu/introd/blob/HEAD/css/vqa_debias_loss_functions.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4bd23f3562cfbd1c","mcp_get_code":{"code_sha256":"4bd23f3562cfbd1c"}},{"arxiv_id":"2010.16010","paper":"/paper/loss-rescaling-vqa-revisiting-language-prior","title":"Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guoyang9/class-imbalance-VQA","path":"utils/losses.py","file_url":"https://github.com/guoyang9/class-imbalance-VQA/blob/HEAD/utils/losses.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4bd23f3562cfbd1c","mcp_get_code":{"code_sha256":"4bd23f3562cfbd1c"}},{"arxiv_id":"2003.06576","paper":"/paper/counterfactual-samples-synthesizing-for","title":"Counterfactual Samples Synthesizing for Robust Visual Question Answering","date":"2020-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4bd23f3562cfbd1c","mcp_get_code":{"code_sha256":"4bd23f3562cfbd1c"}},{"arxiv_id":"1909.03683","paper":"/paper/dont-take-the-easy-way-out-ensemble-based","title":"Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases","date":"2019-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4bd23f3562cfbd1c","mcp_get_code":{"code_sha256":"4bd23f3562cfbd1c"}},{"arxiv_id":"1707.07998","paper":"/paper/bottom-up-and-top-down-attention-for-image","title":"Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering","date":"2017-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chrisc36/bottom-up-attention-vqa","path":"vqa_debias_loss_functions.py","file_url":"https://github.com/chrisc36/bottom-up-attention-vqa/blob/HEAD/vqa_debias_loss_functions.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"4bd23f3562cfbd1c","mcp_get_code":{"code_sha256":"4bd23f3562cfbd1c"}}]}