{"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/to-cpu","entry":"to_cpu","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":16,"n_papers_ran":10,"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":10,"n_samples_ran":4,"n_samples_fingerprinted":2,"n_places":16,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"unverified":6},"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":"2502.01105","paper":"/paper/layertracer-cognitive-aligned-layered-svg","title":"LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/LayerTracer","path":"library/flux_models.py","file_url":"https://github.com/showlab/LayerTracer/blob/HEAD/library/flux_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d563d17c7c16ce57","mcp_get_code":{"code_sha256":"d563d17c7c16ce57"}},{"arxiv_id":"2409.06305","paper":"/paper/high-performance-few-shot-segmentation-with","title":"High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dut-csj/foundationfss","path":"common/utils.py","file_url":"https://github.com/dut-csj/foundationfss/blob/HEAD/common/utils.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":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2408.03972","paper":"/paper/enhancing-output-diversity-improves-conjugate","title":"Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yamamura-k/ReACG","path":"src/core/container.py","file_url":"https://github.com/yamamura-k/ReACG/blob/HEAD/src/core/container.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"afb696106a8b6a2c","mcp_get_code":{"code_sha256":"afb696106a8b6a2c"}},{"arxiv_id":"2407.10542","paper":"/paper/3d-geometric-shape-assembly-via-efficient","title":"3D Geometric Shape Assembly via Efficient Point Cloud Matching","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NahyukLEE/pmtr","path":"common/utils.py","file_url":"https://github.com/NahyukLEE/pmtr/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2405.15265","paper":"/paper/cross-domain-few-shot-semantic-segmentation-1","title":"Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenJiayi68/DMTNet","path":"common/utils.py","file_url":"https://github.com/ChenJiayi68/DMTNet/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2402.17726","paper":"/paper/vrp-sam-sam-with-visual-reference-prompt","title":"VRP-SAM: SAM with Visual Reference Prompt","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syp2ysy/vrp-sam","path":"common/utils.py","file_url":"https://github.com/syp2ysy/vrp-sam/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2402.17614","paper":"/paper/adapt-before-comparison-a-new-perspective-on","title":"Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vision-kek/abcdfss","path":"utils/commonutils.py","file_url":"https://github.com/vision-kek/abcdfss/blob/HEAD/utils/commonutils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2312.15731","paper":"/paper/adaptive-fss-a-novel-few-shot-segmentation","title":"Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingw193/adaptive_fss","path":"common/utils.py","file_url":"https://github.com/jingw193/adaptive_fss/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2309.11568","paper":"/paper/btlm-3b-8k-7b-parameter-performance-in-a-3b","title":"BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model","date":"2023-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cerebras/modelzoo","path":"src/cerebras/modelzoo/common/pytorch_utils.py","file_url":"https://github.com/cerebras/modelzoo/blob/HEAD/src/cerebras/modelzoo/common/pytorch_utils.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":"6e21827728420a8b","mcp_get_code":{"code_sha256":"6e21827728420a8b"}},{"arxiv_id":"2308.13469","paper":"/paper/restnet-boosting-cross-domain-few-shot","title":"RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bupt-ai-cz/restnet","path":"common/utils.py","file_url":"https://github.com/bupt-ai-cz/restnet/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3d18d17a48bf937","mcp_get_code":{"code_sha256":"a3d18d17a48bf937"}},{"arxiv_id":"2302.11893","paper":"/paper/a-framework-for-benchmarking-class-out-of-1","title":"A framework for benchmarking class-out-of-distribution detection and its application to ImageNet","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdabbah/COOD_benchmarking","path":"utils/misc.py","file_url":"https://github.com/mdabbah/COOD_benchmarking/blob/HEAD/utils/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f0964d89a30e7b99","mcp_get_code":{"code_sha256":"f0964d89a30e7b99"}},{"arxiv_id":"2205.13503","paper":"/paper/multi-layer-state-evolution-under-random","title":"Multi-layer State Evolution Under Random Convolutional Design","date":null,"month_inferred_from_arxiv_id":"2022-05","title_source":"archive","repo":"mdnls/conv-ml-amp","path":"arr.py","file_url":"https://github.com/mdnls/conv-ml-amp/blob/HEAD/arr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bf114c8a2834815","mcp_get_code":{"code_sha256":"5bf114c8a2834815"}},{"arxiv_id":"2201.05989","paper":"/paper/instant-neural-graphics-primitives-with-a","title":"Instant Neural Graphics Primitives with a Multiresolution Hash Encoding","date":"2022-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blurgyy/jaxngp","path":"utils/data.py","file_url":"https://github.com/blurgyy/jaxngp/blob/HEAD/utils/data.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":"52787ea8b908e7b9","mcp_get_code":{"code_sha256":"52787ea8b908e7b9"}},{"arxiv_id":"2005.03545","paper":"/paper/misa-modality-invariant-and-specific","title":"MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis","date":"2020-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"declare-lab/MISA","path":"src/utils/convert.py","file_url":"https://github.com/declare-lab/MISA/blob/HEAD/src/utils/convert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1daaefb0404fa995","mcp_get_code":{"code_sha256":"1daaefb0404fa995"}},{"arxiv_id":"2004.10934","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jason-hu7/PyTorch-YOLOv4","path":"src/yolov4/utils.py","file_url":"https://github.com/jason-hu7/PyTorch-YOLOv4/blob/HEAD/src/yolov4/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6499fc14f01a7ceb","mcp_get_code":{"code_sha256":"6499fc14f01a7ceb"}},{"arxiv_id":"2002.08681","paper":"/paper/unsupervised-multi-class-domain-adaptation","title":"Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice","date":"2020-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YBZh/MultiClassDA","path":"utils/utils.py","file_url":"https://github.com/YBZh/MultiClassDA/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e96295481d8e083","mcp_get_code":{"code_sha256":"8e96295481d8e083"}}]}