{"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/dataloader","entry":"dataloader","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":23,"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":39,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":41,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"unverified":34},"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":"2609.02264","paper":"/paper/arxiv-2609-02264","title":"Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"jinxiy1104/CodebookAgent","path":"experiments/run_gsm8k.py","file_url":"https://github.com/jinxiy1104/CodebookAgent/blob/HEAD/experiments/run_gsm8k.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ded2dee441b71d0d","mcp_get_code":{"code_sha256":"ded2dee441b71d0d"}},{"arxiv_id":"2606.02359","paper":"/paper/arxiv-2606-02359","title":"MOC: Multi-Order Communication in LLM-based Multi-Agent Systems","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"yao-guan/MOC","path":"experiments/common.py","file_url":"https://github.com/yao-guan/MOC/blob/HEAD/experiments/common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fa50a6bfb203460","mcp_get_code":{"code_sha256":"9fa50a6bfb203460"}},{"arxiv_id":"2503.00856","paper":"/paper/asymptotic-analysis-of-two-layer-neural","title":"Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure","date":"2025-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KU-MLIP/2-Layer-NNs-with-Gaussian-Mixtures-Data","path":"dataloader.py","file_url":"https://github.com/KU-MLIP/2-Layer-NNs-with-Gaussian-Mixtures-Data/blob/HEAD/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17a13fda48fac95a","mcp_get_code":{"code_sha256":"17a13fda48fac95a"}},{"arxiv_id":"2502.11133","paper":"/paper/masrouter-learning-to-route-llms-for-multi","title":"MasRouter: Learning to Route LLMs for Multi-Agent Systems","date":"2025-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanweiyue/masrouter","path":"Experiments/run_gsm8k.py","file_url":"https://github.com/yanweiyue/masrouter/blob/HEAD/Experiments/run_gsm8k.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":"ded2dee441b71d0d","mcp_get_code":{"code_sha256":"ded2dee441b71d0d"}},{"arxiv_id":"2407.09498","paper":"/paper/ot-vp-optimal-transport-guided-visual","title":"OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zybeich/ot-vp","path":"domainbed/lib/torchmisc.py","file_url":"https://github.com/zybeich/ot-vp/blob/HEAD/domainbed/lib/torchmisc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8722522e3721ebfe","mcp_get_code":{"code_sha256":"8722522e3721ebfe"}},{"arxiv_id":"2311.09071","paper":"/paper/how-multilingual-is-multilingual-llm","title":"How Vocabulary Sharing Facilitates Multilingualism in LLaMA?","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cone-mt/vocabulary-sharing-facilitates-multilingualism","path":"Shorten/src/datasets/ceval.py","file_url":"https://github.com/cone-mt/vocabulary-sharing-facilitates-multilingualism/blob/HEAD/Shorten/src/datasets/ceval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ebf5903eb8c9714b","mcp_get_code":{"code_sha256":"ebf5903eb8c9714b"}},{"arxiv_id":"2306.10989","paper":"/paper/scaling-of-class-wise-training-losses-for","title":"Scaling of Class-wise Training Losses for Post-hoc Calibration","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeungjinJung/SCTL","path":"models/dataloader.py","file_url":"https://github.com/SeungjinJung/SCTL/blob/HEAD/models/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72b0d132e8a4f5c4","mcp_get_code":{"code_sha256":"72b0d132e8a4f5c4"}},{"arxiv_id":"2306.10989","paper":"/paper/scaling-of-class-wise-training-losses-for","title":"Scaling of Class-wise Training Losses for Post-hoc Calibration","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeungjinJung/SCTL","path":"models/utils.py","file_url":"https://github.com/SeungjinJung/SCTL/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"768d7c636555f7fd","mcp_get_code":{"code_sha256":"768d7c636555f7fd"}},{"arxiv_id":"2209.08430","paper":"/paper/dytanvo-joint-refinement-of-visual-odometry","title":"DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments","date":"2022-09-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"geniussh/dytanvo","path":"Datasets/segmask_gt.py","file_url":"https://github.com/geniussh/dytanvo/blob/HEAD/Datasets/segmask_gt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f5b3005dfa2f221f","mcp_get_code":{"code_sha256":"f5b3005dfa2f221f"}},{"arxiv_id":"2208.08914","paper":"/paper/prompt-vision-transformer-for-domain","title":"Prompt Vision Transformer for Domain Generalization","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengzangw/DoPrompt","path":"domainbed/lib/torchmisc.py","file_url":"https://github.com/zhengzangw/DoPrompt/blob/HEAD/domainbed/lib/torchmisc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8722522e3721ebfe","mcp_get_code":{"code_sha256":"8722522e3721ebfe"}},{"arxiv_id":"2104.04314","paper":"/paper/cfnet-cascade-and-fused-cost-volume-for","title":"CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching","date":"2021-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gallenszl/MSMD-Net","path":"datasets/listfiles.py","file_url":"https://github.com/gallenszl/MSMD-Net/blob/HEAD/datasets/listfiles.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0a995994e4e38c4","mcp_get_code":{"code_sha256":"b0a995994e4e38c4"}},{"arxiv_id":"2008.02447","paper":"/paper/functional-regularization-for-representation","title":"Functional Regularization for Representation Learning: A Unified Theoretical Perspective","date":"2020-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sid7954/functional-regularization","path":"auto-encoder/func_reg.py","file_url":"https://github.com/sid7954/functional-regularization/blob/HEAD/auto-encoder/func_reg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d513ea5c05d9fc9d","mcp_get_code":{"code_sha256":"d513ea5c05d9fc9d"}},{"arxiv_id":"2007.03085","paper":"/paper/wasserstein-distances-for-stereo-disparity","title":"Wasserstein Distances for Stereo Disparity Estimation","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Div99/W-Stereo-Disp","path":"src/disp_dataloader/KITTILoader3D.py","file_url":"https://github.com/Div99/W-Stereo-Disp/blob/HEAD/src/disp_dataloader/KITTILoader3D.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"74a08f63b12add42","mcp_get_code":{"code_sha256":"74a08f63b12add42"}},{"arxiv_id":"2006.00873","paper":"/paper/a-generalised-signature-method-for-time","title":"A Generalised Signature Method for Multivariate Time Series Feature Extraction","date":"2020-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jambo6/generalised-signature-method","path":"get_data/speech_commands.py","file_url":"https://github.com/jambo6/generalised-signature-method/blob/HEAD/get_data/speech_commands.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"951e854ee3f4793f","mcp_get_code":{"code_sha256":"951e854ee3f4793f"}},{"arxiv_id":"2005.08926","paper":"/paper/neural-controlled-differential-equations-for","title":"Neural Controlled Differential Equations for Irregular Time Series","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"patrick-kidger/NeuralCDE","path":"experiments/datasets/common.py","file_url":"https://github.com/patrick-kidger/NeuralCDE/blob/HEAD/experiments/datasets/common.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":"3d00ef924475de36","mcp_get_code":{"code_sha256":"3d00ef924475de36"}},{"arxiv_id":"2005.08926","paper":"/paper/neural-controlled-differential-equations-for","title":"Neural Controlled Differential Equations for Irregular Time Series","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dungxibo123/NeuralCDE","path":"experiments/datasets/common.py","file_url":"https://github.com/dungxibo123/NeuralCDE/blob/HEAD/experiments/datasets/common.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":"3588c9b656c72587","mcp_get_code":{"code_sha256":"3588c9b656c72587"}},{"arxiv_id":"1912.06704","paper":"/paper/hierarchical-deep-stereo-matching-on-high-1","title":"Hierarchical Deep Stereo Matching on High-resolution Images","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengshan-y/high-res-stereo","path":"dataloader/KITTIloader2012.py","file_url":"https://github.com/gengshan-y/high-res-stereo/blob/HEAD/dataloader/KITTIloader2012.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7dfd7be48993bd31","mcp_get_code":{"code_sha256":"7dfd7be48993bd31"}},{"arxiv_id":"1912.06704","paper":"/paper/hierarchical-deep-stereo-matching-on-high-1","title":"Hierarchical Deep Stereo Matching on High-resolution Images","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengshan-y/high-res-stereo","path":"dataloader/KITTIloader2015.py","file_url":"https://github.com/gengshan-y/high-res-stereo/blob/HEAD/dataloader/KITTIloader2015.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f37c6395f8cb912","mcp_get_code":{"code_sha256":"1f37c6395f8cb912"}},{"arxiv_id":"1912.06704","paper":"/paper/hierarchical-deep-stereo-matching-on-high-1","title":"Hierarchical Deep Stereo Matching on High-resolution Images","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengshan-y/high-res-stereo","path":"dataloader/listfiles.py","file_url":"https://github.com/gengshan-y/high-res-stereo/blob/HEAD/dataloader/listfiles.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89def291c40ceb41","mcp_get_code":{"code_sha256":"89def291c40ceb41"}},{"arxiv_id":"1912.06704","paper":"/paper/hierarchical-deep-stereo-matching-on-high-1","title":"Hierarchical Deep Stereo Matching on High-resolution Images","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gengshan-y/high-res-stereo","path":"dataloader/listsceneflow.py","file_url":"https://github.com/gengshan-y/high-res-stereo/blob/HEAD/dataloader/listsceneflow.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93ba7c1654dce94b","mcp_get_code":{"code_sha256":"93ba7c1654dce94b"}},{"arxiv_id":"1911.07123","paper":"/paper/graph-revised-convolutional-network","title":"Graph-Revised Convolutional Network","date":"2019-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plusross/grcn","path":"dataprocess.py","file_url":"https://github.com/plusross/grcn/blob/HEAD/dataprocess.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0822b0c72696f378","mcp_get_code":{"code_sha256":"0822b0c72696f378"}},{"arxiv_id":"1911.04460","paper":"/paper/360sd-net-360-stereo-depth-estimation-with","title":"360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume","date":"2019-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albert100121/360SD-Net","path":"dataloader/filename_loader.py","file_url":"https://github.com/albert100121/360SD-Net/blob/HEAD/dataloader/filename_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f98e3ffc934fb62","mcp_get_code":{"code_sha256":"2f98e3ffc934fb62"}},{"arxiv_id":"1911.04460","paper":"/paper/360sd-net-360-stereo-depth-estimation-with","title":"360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume","date":"2019-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albert100121/360SD-Net","path":"dataloader/testing_loader.py","file_url":"https://github.com/albert100121/360SD-Net/blob/HEAD/dataloader/testing_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c244e29f33dfb60c","mcp_get_code":{"code_sha256":"c244e29f33dfb60c"}},{"arxiv_id":"1906.06310","paper":"/paper/pseudo-lidar-accurate-depth-for-3d-object","title":"Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/Pseudo_Lidar_V2","path":"src/dataloader/KITTILoader3D.py","file_url":"https://github.com/mileyan/Pseudo_Lidar_V2/blob/HEAD/src/dataloader/KITTILoader3D.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3afe1d264a83296","mcp_get_code":{"code_sha256":"a3afe1d264a83296"}},{"arxiv_id":"1902.10197","paper":"/paper/rotate-knowledge-graph-embedding-by","title":"RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davendw49/gakg","path":"code/baselines/transE/TransE_pytoch.py","file_url":"https://github.com/davendw49/gakg/blob/HEAD/code/baselines/transE/TransE_pytoch.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":"0577997988d41269","mcp_get_code":{"code_sha256":"0577997988d41269"}},{"arxiv_id":"1902.10197","paper":"/paper/rotate-knowledge-graph-embedding-by","title":"RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space","date":"2019-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davendw49/gakg","path":"code/baselines/transE/transE.py","file_url":"https://github.com/davendw49/gakg/blob/HEAD/code/baselines/transE/transE.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":"dbe17b25039229ed","mcp_get_code":{"code_sha256":"dbe17b25039229ed"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/KITTIloader2012.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/KITTIloader2012.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"337eab65fc60395a","mcp_get_code":{"code_sha256":"337eab65fc60395a"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/KITTIloader2015.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/KITTIloader2015.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"164ddc7f85f5e375","mcp_get_code":{"code_sha256":"164ddc7f85f5e375"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/diy_dataset.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/diy_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f372bd977f385406","mcp_get_code":{"code_sha256":"f372bd977f385406"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mileyan/AnyNet","path":"dataloader/listflowfile.py","file_url":"https://github.com/mileyan/AnyNet/blob/HEAD/dataloader/listflowfile.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa7e13ed905b10f3","mcp_get_code":{"code_sha256":"aa7e13ed905b10f3"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mamoanwar97/Anynet_modified","path":"dataloader/KITTI_dataset.py","file_url":"https://github.com/mamoanwar97/Anynet_modified/blob/HEAD/dataloader/KITTI_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b59b91ff9420dc34","mcp_get_code":{"code_sha256":"b59b91ff9420dc34"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mamoanwar97/Anynet_modified","path":"dataloader/KITTIloader2012.py","file_url":"https://github.com/mamoanwar97/Anynet_modified/blob/HEAD/dataloader/KITTIloader2012.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86dc9ef96c0417c9","mcp_get_code":{"code_sha256":"86dc9ef96c0417c9"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mamoanwar97/Anynet_modified","path":"dataloader/KITTIloader2015.py","file_url":"https://github.com/mamoanwar97/Anynet_modified/blob/HEAD/dataloader/KITTIloader2015.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0212e19ca5cc4ab5","mcp_get_code":{"code_sha256":"0212e19ca5cc4ab5"}},{"arxiv_id":"1810.11408","paper":"/paper/anytime-stereo-image-depth-estimation-on","title":"Anytime Stereo Image Depth Estimation on Mobile Devices","date":"2018-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajeevpatwari/anynet","path":"dataloader/listflowfile.py","file_url":"https://github.com/rajeevpatwari/anynet/blob/HEAD/dataloader/listflowfile.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2502fc54633e5cb4","mcp_get_code":{"code_sha256":"2502fc54633e5cb4"}},{"arxiv_id":"1807.02758","paper":"/paper/image-super-resolution-using-very-deep","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","date":"2018-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dongheehand/RCAN-tf","path":"RCA_net.py","file_url":"https://github.com/dongheehand/RCAN-tf/blob/HEAD/RCA_net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c214ddcde10c7d20","mcp_get_code":{"code_sha256":"c214ddcde10c7d20"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTI_submission_loader.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTI_submission_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70963b30440822d8","mcp_get_code":{"code_sha256":"70963b30440822d8"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTI_submission_loader2012.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTI_submission_loader2012.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a9e2227f81699e9","mcp_get_code":{"code_sha256":"9a9e2227f81699e9"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTIloader2012.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTIloader2012.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"facba429ec9a5e75","mcp_get_code":{"code_sha256":"facba429ec9a5e75"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/KITTIloader2015.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/KITTIloader2015.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"71cbc3b4bc1e8f34","mcp_get_code":{"code_sha256":"71cbc3b4bc1e8f34"}},{"arxiv_id":"1803.08669","paper":"/paper/pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiaRenChang/PSMNet","path":"dataloader/listflowfile.py","file_url":"https://github.com/JiaRenChang/PSMNet/blob/HEAD/dataloader/listflowfile.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"722050d7e9df5e9c","mcp_get_code":{"code_sha256":"722050d7e9df5e9c"}},{"arxiv_id":"1410.5401","paper":"/paper/neural-turing-machines","title":"Neural Turing Machines","date":"2014-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"loudinthecloud/pytorch-ntm","path":"tasks/repeatcopytask.py","file_url":"https://github.com/loudinthecloud/pytorch-ntm/blob/HEAD/tasks/repeatcopytask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"b5063f141af96e2f","mcp_get_code":{"code_sha256":"b5063f141af96e2f"}}]}