{"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/get-emb","entry":"get_emb","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":20,"n_papers_ran":15,"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":1,"n_places":21,"n_places_pointer_only":8,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2603.04317","paper":"/paper/arxiv-2603-04317","title":"World Properties without World Models: Recovering Spatial and Temporal Structure from Co-occurrence Statistics in Static Word Embeddings","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"elanbarenholtz/static-embeddings-space-time","path":"glove_multi_probe.py","file_url":"https://github.com/elanbarenholtz/static-embeddings-space-time/blob/HEAD/glove_multi_probe.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8d30e013152acdb","mcp_get_code":{"code_sha256":"c8d30e013152acdb"}},{"arxiv_id":"2602.00407","paper":"/paper/arxiv-2602-00407","title":"Fed-Listing: Federated Label Distribution Inference in Graph Neural Networks","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"suprimnakarmi/Fed-Listing","path":"source_code/baselines/EC-LDA-node-classification/iLRG/methods.py","file_url":"https://github.com/suprimnakarmi/Fed-Listing/blob/HEAD/source_code/baselines/EC-LDA-node-classification/iLRG/methods.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d2dd16caa88e2faa","mcp_get_code":{"code_sha256":"d2dd16caa88e2faa"}},{"arxiv_id":"2411.00066","paper":"/paper/interpretable-language-modeling-via-induction","title":"Interpretable Language Modeling via Induction-head Ngram Models","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ejkim47/induction-gram","path":"alm/models/mini_gpt.py","file_url":"https://github.com/ejkim47/induction-gram/blob/HEAD/alm/models/mini_gpt.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2409.16288","paper":"/paper/self-supervised-any-point-tracking-by","title":"Self-Supervised Any-Point Tracking by Contrastive Random Walks","date":"2024-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayshrv/gmrw","path":"models/gmflow_model/position.py","file_url":"https://github.com/ayshrv/gmrw/blob/HEAD/models/gmflow_model/position.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2407.13335","paper":"/paper/oat-object-level-attention-transformer-for","title":"OAT: Object-Level Attention Transformer for Gaze Scanpath Prediction","date":"2024-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-nisl/oat_eccv24","path":"src/model/positionalEncoding.py","file_url":"https://github.com/hkust-nisl/oat_eccv24/blob/HEAD/src/model/positionalEncoding.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2406.08380","paper":"/paper/towards-unsupervised-speech-recognition","title":"Towards Unsupervised Speech Recognition Without Pronunciation Models","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeromeni/wholeword-uasr-jstti","path":"GradSeg/data_loader.py","file_url":"https://github.com/jeromeni/wholeword-uasr-jstti/blob/HEAD/GradSeg/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a73490164e4bb95c","mcp_get_code":{"code_sha256":"a73490164e4bb95c"}},{"arxiv_id":"2405.07395","paper":"/paper/cafa-global-weather-forecasting-with","title":"CaFA: Global Weather Forecasting with Factorized Attention on Sphere","date":"2024-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaratiLab/CaFA","path":"libs/positional_encoding_module.py","file_url":"https://github.com/BaratiLab/CaFA/blob/HEAD/libs/positional_encoding_module.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2402.04396","paper":"/paper/quip-even-better-llm-quantization-with","title":"QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cornell-RelaxML/quip-sharp","path":"quantize_llama/finetune_e2e_llama.py","file_url":"https://github.com/Cornell-RelaxML/quip-sharp/blob/HEAD/quantize_llama/finetune_e2e_llama.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"71304b57668d33e4","mcp_get_code":{"code_sha256":"71304b57668d33e4"}},{"arxiv_id":"2312.04552","paper":"/paper/generating-illustrated-instructions","title":"Generating Illustrated Instructions","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sachit-menon/generating-illustrated-instructions-reproduction","path":"layers.py","file_url":"https://github.com/sachit-menon/generating-illustrated-instructions-reproduction/blob/HEAD/layers.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2311.08863","paper":"/paper/toulouse-hyperspectral-data-set-a-benchmark","title":"Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"romain3ch216/tlse-experiments","path":"models/utils.py","file_url":"https://github.com/romain3ch216/tlse-experiments/blob/HEAD/models/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2311.00136","paper":"/paper/neuroformer-multimodal-and-multitask","title":"Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"a-antoniades/neuroformer","path":"neuroformer/model_neuroformer.py","file_url":"https://github.com/a-antoniades/neuroformer/blob/HEAD/neuroformer/model_neuroformer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2310.20030","paper":"/paper/scaling-riemannian-diffusion-models","title":"Scaling Riemannian Diffusion Models","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"louaaron/Scaling-Riemannian-Diffusion","path":"lattice_qcd/model.py","file_url":"https://github.com/louaaron/Scaling-Riemannian-Diffusion/blob/HEAD/lattice_qcd/model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2303.15269","paper":"/paper/handwritten-text-generation-from-visual","title":"Handwritten Text Generation from Visual Archetypes","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/VATr","path":"models/model.py","file_url":"https://github.com/aimagelab/VATr/blob/HEAD/models/model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2303.12733","paper":"/paper/on-the-de-duplication-of-laion-2b","title":"On the De-duplication of LAION-2B","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryanwebster90/snip-dedup","path":"retrieve_dup_urls_demo.py","file_url":"https://github.com/ryanwebster90/snip-dedup/blob/HEAD/retrieve_dup_urls_demo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e236fba907d13b4","mcp_get_code":{"code_sha256":"8e236fba907d13b4"}},{"arxiv_id":"2211.13220","paper":"/paper/tetrahedral-diffusion-models-for-3d-shape","title":"TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeterTor/TetraDiffusion","path":"lib/UVIT.py","file_url":"https://github.com/PeterTor/TetraDiffusion/blob/HEAD/lib/UVIT.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"33805df68cb6892e","mcp_get_code":{"code_sha256":"33805df68cb6892e"}},{"arxiv_id":"2211.03329","paper":"/paper/implicit-graphon-neural-representation","title":"Implicit Graphon Neural Representation","date":"2022-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mishne-lab/ignr","path":"c-IGNR/evaluation.py","file_url":"https://github.com/mishne-lab/ignr/blob/HEAD/c-IGNR/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b444e94d464426b5","mcp_get_code":{"code_sha256":"b444e94d464426b5"}},{"arxiv_id":"2204.13094","paper":"/paper/unsupervised-word-segmentation-using-k","title":"Unsupervised Word Segmentation using K Nearest Neighbors","date":null,"month_inferred_from_arxiv_id":"2022-04","title_source":"archive","repo":"mlspeech/gradseg","path":"data_loader.py","file_url":"https://github.com/mlspeech/gradseg/blob/HEAD/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a73490164e4bb95c","mcp_get_code":{"code_sha256":"a73490164e4bb95c"}},{"arxiv_id":"2112.05194","paper":"/paper/word-embeddings-via-causal-inference-gender","title":"Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lei-Ding07/Word_Debias_DeSIP","path":"load_embedding.py","file_url":"https://github.com/Lei-Ding07/Word_Debias_DeSIP/blob/HEAD/load_embedding.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7722c58b6cc84546","mcp_get_code":{"code_sha256":"7722c58b6cc84546"}},{"arxiv_id":"1911.01196","paper":"/paper/spherical-text-embedding","title":"Spherical Text Embedding","date":"2019-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yumeng5/Spherical-Text-Embedding","path":"sim.py","file_url":"https://github.com/yumeng5/Spherical-Text-Embedding/blob/HEAD/sim.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7722c58b6cc84546","mcp_get_code":{"code_sha256":"7722c58b6cc84546"}},{"arxiv_id":"1911.01196","paper":"/paper/spherical-text-embedding","title":"Spherical Text Embedding","date":"2019-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yumeng5/Spherical-Text-Embedding","path":"classify.py","file_url":"https://github.com/yumeng5/Spherical-Text-Embedding/blob/HEAD/classify.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":"c152449a72ec7914","mcp_get_code":{"code_sha256":"c152449a72ec7914"}},{"arxiv_id":"1805.08318","paper":"/paper/self-attention-generative-adversarial","title":"Self-Attention Generative Adversarial Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alfagao/DeOldify","path":"fastai/column_data.py","file_url":"https://github.com/alfagao/DeOldify/blob/HEAD/fastai/column_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e0a726be884ad1d","mcp_get_code":{"code_sha256":"2e0a726be884ad1d"}}]}