{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/test/papers/40","list_of":"/method/test","method":"Test","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":40,"pages_in_order":65,"rows_per_page":100,"rows":[3901,4000],"of":6434,"counts":{"archive_papers_tagged":6434,"with_a_code_link":2339,"where_syntology_ran_a_sample":517,"not_listed_spam_title":0,"listed":6434,"listed_where_code_ran":517,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":428,"every_run_a_failure_of_syntologys_instrument":89,"listed_with_a_run_with_no_instrument_failure":428,"listed_every_run_a_failure_of_syntologys_instrument":89,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/test","prev":"/method/test/papers/39","next":"/method/test/papers/41","papers":[{"paper":null,"slug":"coordinated-control-in-multi-terminal-vsc","title":"Coordinated control in multi-terminal VSC-HVDC systems to improve transient stability: Impact on electromechanical-oscillation damping","date":"2022-07-29","arxiv_id":"2208.00083","n_code_links":0,"syntology":null},{"paper":"/paper/domain-specific-wav2vec-2-0-fine-tuning-for","slug":"domain-specific-wav2vec-2-0-fine-tuning-for","title":"Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 Challenge","date":"2022-07-29","arxiv_id":"2207.14418","n_code_links":1,"syntology":null},{"paper":null,"slug":"factorizable-joint-shift-in-multinomial","title":"Factorizable Joint Shift in Multinomial Classification","date":"2022-07-29","arxiv_id":"2207.14514","n_code_links":0,"syntology":null},{"paper":"/paper/language-models-can-teach-themselves-to","slug":"language-models-can-teach-themselves-to","title":"Language Models Can Teach Themselves to Program Better","date":"2022-07-29","arxiv_id":"2207.14502","n_code_links":1,"syntology":null},{"paper":"/paper/restoring-vision-in-adverse-weather","slug":"restoring-vision-in-adverse-weather","title":"Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models","date":"2022-07-29","arxiv_id":"2207.14626","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":5,"n_instrument":5,"unverified":1,"pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","official":{"repos":["igitugraz/weatherdiffusion"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"thutmose-tagger-single-pass-neural-model-for","title":"Thutmose Tagger: Single-pass neural model for Inverse Text Normalization","date":"2022-07-29","arxiv_id":"2208.00064","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-data-augmentation-technique-for-out","slug":"a-novel-data-augmentation-technique-for-out","title":"A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions","date":"2022-07-28","arxiv_id":"2207.13916","n_code_links":1,"syntology":{"ran":7,"of":14,"n_ran_checked":6,"n_instrument":1,"unverified":7,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["cnc-ood/cnc_ood"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"benefits-and-limitations-of-a-new-hydraulic","title":"Benefits and limitations of a new hydraulic performance model","date":"2022-07-28","arxiv_id":"2207.14295","n_code_links":0,"syntology":null},{"paper":"/paper/bridging-the-gap-between-deep-learning-and-1","slug":"bridging-the-gap-between-deep-learning-and-1","title":"Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing","date":"2022-07-28","arxiv_id":"2207.14349","n_code_links":1,"syntology":null},{"paper":null,"slug":"construction-of-multi-period-tso-dso","title":"Construction of Multi-period TSO-DSO Flexibility Regions","date":"2022-07-28","arxiv_id":"2207.14203","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-acoustic-mosquito","slug":"deep-learning-based-acoustic-mosquito","title":"Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations","date":"2022-07-28","arxiv_id":"2207.13843","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-for-understanding-multilabel","title":"Deep learning for understanding multilabel imbalanced Chest X-ray datasets","date":"2022-07-28","arxiv_id":"2207.14408","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-neural-networks-to-predict-sports","title":"Graph Neural Networks to Predict Sports Outcomes","date":"2022-07-28","arxiv_id":"2207.14124","n_code_links":0,"syntology":null},{"paper":"/paper/improving-the-performance-of-robust-control","slug":"improving-the-performance-of-robust-control","title":"Improving the Performance of Robust Control through Event-Triggered Learning","date":"2022-07-28","arxiv_id":"2207.14252","n_code_links":1,"syntology":null},{"paper":null,"slug":"large-language-models-and-the-reverse-turing","title":"Large Language Models and the Reverse Turing Test","date":"2022-07-28","arxiv_id":"2207.14382","n_code_links":0,"syntology":null},{"paper":"/paper/multi-step-deductive-reasoning-over-natural","slug":"multi-step-deductive-reasoning-over-natural","title":"Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation","date":"2022-07-28","arxiv_id":"2207.14000","n_code_links":1,"syntology":null},{"paper":null,"slug":"playing-a-2d-game-indefinitely-using-neat-and","title":"Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning","date":"2022-07-28","arxiv_id":"2207.14140","n_code_links":0,"syntology":null},{"paper":"/paper/predicting-the-output-structure-of-sparse","slug":"predicting-the-output-structure-of-sparse","title":"Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio","date":"2022-07-28","arxiv_id":"2207.13848","n_code_links":1,"syntology":null},{"paper":"/paper/rewriting-geometric-rules-of-a-gan","slug":"rewriting-geometric-rules-of-a-gan","title":"Rewriting Geometric Rules of a GAN","date":"2022-07-28","arxiv_id":"2207.14288","n_code_links":1,"syntology":{"ran":10,"of":14,"n_ran_checked":10,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["peterwang512/ganwarping"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"semi-supervised-learning-of-partial-1","title":"Semi-supervised Learning of Partial Differential Operators and Dynamical Flows","date":"2022-07-28","arxiv_id":"2207.14366","n_code_links":0,"syntology":null},{"paper":null,"slug":"separable-quaternion-matrix-factorization-for","title":"Separable Quaternion Matrix Factorization for Polarization Images","date":"2022-07-28","arxiv_id":"2207.14039","n_code_links":0,"syntology":null},{"paper":"/paper/supervessel-segmenting-high-resolution-vessel","slug":"supervessel-segmenting-high-resolution-vessel","title":"SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image","date":"2022-07-28","arxiv_id":"2207.13882","n_code_links":2,"syntology":null},{"paper":"/paper/video-mask-transfiner-for-high-quality-video","slug":"video-mask-transfiner-for-high-quality-video","title":"Video Mask Transfiner for High-Quality Video Instance Segmentation","date":"2022-07-28","arxiv_id":"2207.14012","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":2,"n_instrument":3,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"3d-morphomics-morphological-features-on-ct","title":"3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis","date":"2022-07-27","arxiv_id":"2207.13830","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-semi-automatic-cell-tracking-process","title":"A Semi-automatic Cell Tracking Process Towards Completing the 4D Atlas of C. elegans Development","date":"2022-07-27","arxiv_id":"2207.13611","n_code_links":0,"syntology":null},{"paper":null,"slug":"applied-computer-vision-on-2-dimensional-lung","title":"Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia","date":"2022-07-27","arxiv_id":"2207.13295","n_code_links":0,"syntology":null},{"paper":null,"slug":"encoding-concepts-in-graph-neural-networks","title":"Encoding Concepts in Graph Neural Networks","date":"2022-07-27","arxiv_id":"2207.13586","n_code_links":0,"syntology":null},{"paper":null,"slug":"internet-of-things-iot-based-ecg-system-for","title":"Internet of Things (IoT) based ECG System for Rural Health Care","date":"2022-07-27","arxiv_id":"2208.02226","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-driven-subword-grammar-modeling-for","title":"Knowledge-driven Subword Grammar Modeling for Automatic Speech Recognition in Tamil and Kannada","date":"2022-07-27","arxiv_id":"2207.13333","n_code_links":0,"syntology":null},{"paper":"/paper/lightweight-and-progressively-scalable","slug":"lightweight-and-progressively-scalable","title":"Lightweight and Progressively-Scalable Networks for Semantic Segmentation","date":"2022-07-27","arxiv_id":"2207.13600","n_code_links":1,"syntology":null},{"paper":null,"slug":"nicest-noisy-label-correction-and-training","title":"NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation","date":"2022-07-27","arxiv_id":"2207.13316","n_code_links":0,"syntology":null},{"paper":"/paper/safe-and-robust-experience-sharing-for","slug":"safe-and-robust-experience-sharing-for","title":"Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms","date":"2022-07-27","arxiv_id":"2207.13453","n_code_links":1,"syntology":null},{"paper":null,"slug":"subword-dictionary-learning-and-segmentation","title":"Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada","date":"2022-07-27","arxiv_id":"2207.13331","n_code_links":0,"syntology":null},{"paper":null,"slug":"traffic-sign-detection-with-event-cameras-and","title":"Traffic Sign Detection With Event Cameras and DCNN","date":"2022-07-27","arxiv_id":"2207.13345","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-the-use-of-edge-tpus-for-eye","title":"A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation","date":"2022-07-26","arxiv_id":"2207.12770","n_code_links":0,"syntology":null},{"paper":"/paper/debiasing-deep-chest-x-ray-classifiers-using","slug":"debiasing-deep-chest-x-ray-classifiers-using","title":"Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods","date":"2022-07-26","arxiv_id":"2208.00781","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-model-based-architectures-for-inverse","title":"Deep Model-Based Architectures for Inverse Problems under Mismatched Priors","date":"2022-07-26","arxiv_id":"2207.13200","n_code_links":0,"syntology":null},{"paper":null,"slug":"enabling-grid-aware-market-participation-of","title":"Enabling Grid-Aware Market Participation of Aggregate Flexible Resources","date":"2022-07-26","arxiv_id":"2207.12685","n_code_links":0,"syntology":null},{"paper":"/paper/finding-deep-learning-compilation-bugs-with","slug":"finding-deep-learning-compilation-bugs-with","title":"NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers","date":"2022-07-26","arxiv_id":"2207.13066","n_code_links":1,"syntology":null},{"paper":"/paper/hansel-a-chinese-few-shot-and-zero-shot-1","slug":"hansel-a-chinese-few-shot-and-zero-shot-1","title":"Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark","date":"2022-07-26","arxiv_id":"2207.13005","n_code_links":1,"syntology":null},{"paper":"/paper/physical-systems-modeled-without-physical","slug":"physical-systems-modeled-without-physical","title":"Physical Systems Modeled Without Physical Laws","date":"2022-07-26","arxiv_id":"2207.13702","n_code_links":1,"syntology":null},{"paper":"/paper/physics-informed-neural-networks-for-shell","slug":"physics-informed-neural-networks-for-shell","title":"Physics-Informed Neural Networks for Shell Structures","date":"2022-07-26","arxiv_id":"2207.14291","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jhbastek/PhysicsInformedShellStructures"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"remote-medication-status-prediction-for","title":"Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones","date":"2022-07-26","arxiv_id":"2207.13700","n_code_links":0,"syntology":null},{"paper":null,"slug":"spline-shaped-microstrip-edge-fed-antenna-for","title":"Spline-Shaped Microstrip Edge-Fed Antenna for 77 GHz Automotive Radar Systems","date":"2022-07-26","arxiv_id":"2208.00841","n_code_links":0,"syntology":null},{"paper":null,"slug":"task-agnostic-and-post-hoc-unseen","title":"Task Agnostic and Post-hoc Unseen Distribution Detection","date":"2022-07-26","arxiv_id":"2207.13083","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-sample-based-algorithm-for-approximately","title":"A Sample-Based Algorithm for Approximately Testing $r$-Robustness of a Digraph","date":"2022-07-25","arxiv_id":"2207.12110","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-powered-anti-cyber-bullying-system-using","title":"AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine","date":"2022-07-25","arxiv_id":"2207.11897","n_code_links":0,"syntology":null},{"paper":"/paper/an-empirical-deep-dive-into-deep-learning-s","slug":"an-empirical-deep-dive-into-deep-learning-s","title":"The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks","date":"2022-07-25","arxiv_id":"2207.12547","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-encryption-method-of-convmixer-models","title":"An Encryption Method of ConvMixer Models without Performance Degradation","date":"2022-07-25","arxiv_id":"2207.11939","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-algorithm-for-assessment","title":"Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists","date":"2022-07-25","arxiv_id":"2207.12521","n_code_links":0,"syntology":null},{"paper":"/paper/differential-testing-for-machine-learning-an","slug":"differential-testing-for-machine-learning-an","title":"Differential testing for machine learning: an analysis for classification algorithms beyond deep learning","date":"2022-07-25","arxiv_id":"2207.11976","n_code_links":1,"syntology":null},{"paper":"/paper/domain-decorrelation-with-potential-energy","slug":"domain-decorrelation-with-potential-energy","title":"Domain Decorrelation with Potential Energy Ranking","date":"2022-07-25","arxiv_id":"2207.12194","n_code_links":1,"syntology":null},{"paper":"/paper/domain-invariant-feature-exploration-for","slug":"domain-invariant-feature-exploration-for","title":"Domain-invariant Feature Exploration for Domain Generalization","date":"2022-07-25","arxiv_id":"2207.12020","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jindongwang/transferlearning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hardware-in-the-loop-simulation-of-a-uav","title":"Hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA","date":"2022-07-25","arxiv_id":"2207.12198","n_code_links":0,"syntology":null},{"paper":null,"slug":"localization-of-coordinated-cyber-physical","title":"Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning","date":"2022-07-25","arxiv_id":"2207.12339","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-to-predict-the-antimicrobial","title":"Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids","date":"2022-07-25","arxiv_id":"2207.12478","n_code_links":0,"syntology":null},{"paper":null,"slug":"natural-disasters-entrepreneurship-activity","title":"Natural Disasters, Entrepreneurship Activity, and the Moderating Role of Country Governance","date":"2022-07-25","arxiv_id":"2207.12492","n_code_links":0,"syntology":null},{"paper":"/paper/openran-gym-ai-ml-development-data-collection","slug":"openran-gym-ai-ml-development-data-collection","title":"OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms","date":"2022-07-25","arxiv_id":"2207.12362","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimal-utilization-of-third-party-demand","title":"Optimal Utilization of Third-Party Demand Response Resources in Vertically Integrated Utilities: A Game Theoretic Approach","date":"2022-07-25","arxiv_id":"2207.11975","n_code_links":0,"syntology":null},{"paper":"/paper/seeing-far-in-the-dark-with-patterned-flash","slug":"seeing-far-in-the-dark-with-patterned-flash","title":"Seeing Far in the Dark with Patterned Flash","date":"2022-07-25","arxiv_id":"2207.12570","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-ecg-denoising-scheme-using-the","title":"A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter","date":"2022-07-24","arxiv_id":"2207.11819","n_code_links":0,"syntology":null},{"paper":"/paper/armanemo-a-persian-dataset-for-text-based","slug":"armanemo-a-persian-dataset-for-text-based","title":"ArmanEmo: A Persian Dataset for Text-based Emotion Detection","date":"2022-07-24","arxiv_id":"2207.11808","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-multi-label-learning-to-cross-domain","title":"From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach","date":"2022-07-24","arxiv_id":"2207.11742","n_code_links":0,"syntology":null},{"paper":"/paper/label-guided-auxiliary-training-improves-3d","slug":"label-guided-auxiliary-training-improves-3d","title":"Label-Guided Auxiliary Training Improves 3D Object Detector","date":"2022-07-24","arxiv_id":"2207.11753","n_code_links":1,"syntology":null},{"paper":"/paper/pose-forecasting-in-industrial-human-robot","slug":"pose-forecasting-in-industrial-human-robot","title":"Pose Forecasting in Industrial Human-Robot Collaboration","date":"2022-07-24","arxiv_id":"2208.07308","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":7,"n_instrument":0,"unverified":2,"pointer_only":4,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["alessiosam/chico-poseforecasting"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"paper":null,"slug":"a-deployable-online-optimization-framework","title":"A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases","date":"2022-07-23","arxiv_id":"2207.11403","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-impact-of-downsizing-on","title":"A Study on Impact of Downsizing on Profitability of Construction Industries listed in Bombay Stock Exchange (BSE) India","date":"2022-07-23","arxiv_id":"2207.11546","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-universal-trade-off-between-the-model-size","title":"A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors","date":"2022-07-23","arxiv_id":"2207.11621","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-common-bubbles-in-multivariate","title":"Detecting common bubbles in multivariate mixed causal-noncausal models","date":"2022-07-23","arxiv_id":"2207.11557","n_code_links":0,"syntology":null},{"paper":"/paper/distributed-nonlinear-state-estimation-in","slug":"distributed-nonlinear-state-estimation-in","title":"Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks","date":"2022-07-23","arxiv_id":"2207.11465","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-route-in-mobile-wireless-networks","title":"Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency","date":"2022-07-23","arxiv_id":"2207.11386","n_code_links":0,"syntology":null},{"paper":"/paper/low-complexity-acoustic-echo-cancellation","slug":"low-complexity-acoustic-echo-cancellation","title":"Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering","date":"2022-07-23","arxiv_id":"2207.11388","n_code_links":1,"syntology":null},{"paper":null,"slug":"variational-temporal-deconfounder-for","title":"Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data","date":"2022-07-23","arxiv_id":"2207.11251","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-conditional-linear-combination-test-with","title":"A Conditional Linear Combination Test with Many Weak Instruments","date":"2022-07-22","arxiv_id":"2207.11137","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-meta-predictor-based-algorithm-for","title":"HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation","date":"2022-07-22","arxiv_id":"2207.11312","n_code_links":0,"syntology":null},{"paper":null,"slug":"applying-spatiotemporal-attention-to-identify","title":"Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers","date":"2022-07-22","arxiv_id":"2207.12148","n_code_links":0,"syntology":null},{"paper":"/paper/do-perceptually-aligned-gradients-imply","slug":"do-perceptually-aligned-gradients-imply","title":"Do Perceptually Aligned Gradients Imply Adversarial Robustness?","date":"2022-07-22","arxiv_id":"2207.11378","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["royg27/pag-rob"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"e2n-error-estimation-networks-for-goal","title":"E2N: Error Estimation Networks for Goal-Oriented Mesh Adaptation","date":"2022-07-22","arxiv_id":"2207.11233","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-testing-of-deep-neural-networks-via","slug":"efficient-testing-of-deep-neural-networks-via","title":"Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation","date":"2022-07-22","arxiv_id":"2207.10942","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-predictive-performance-and","title":"Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation","date":"2022-07-22","arxiv_id":"2207.11211","n_code_links":0,"syntology":null},{"paper":"/paper/infinitenature-zero-learning-perpetual-view","slug":"infinitenature-zero-learning-perpetual-view","title":"InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images","date":"2022-07-22","arxiv_id":"2207.11148","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-generalized-non-rigid-multimodal","title":"Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data","date":"2022-07-22","arxiv_id":"2207.10994","n_code_links":0,"syntology":null},{"paper":null,"slug":"mechanics-of-morphogenesis-in-neural","title":"Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and in silico","date":"2022-07-22","arxiv_id":"2207.10861","n_code_links":0,"syntology":null},{"paper":null,"slug":"meta-registration-learning-test-time","title":"Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration","date":"2022-07-22","arxiv_id":"2207.10996","n_code_links":0,"syntology":null},{"paper":null,"slug":"meter-ml-a-multi-sensor-earth-observation","title":"METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping","date":"2022-07-22","arxiv_id":"2207.11166","n_code_links":0,"syntology":null},{"paper":"/paper/neural-sim-learning-to-generate-training-data","slug":"neural-sim-learning-to-generate-training-data","title":"Neural-Sim: Learning to Generate Training Data with NeRF","date":"2022-07-22","arxiv_id":"2207.11368","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["gyhandy/neural-sim-nerf"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"seeing-3d-objects-in-a-single-image-via-self","title":"Neural Groundplans: Persistent Neural Scene Representations from a Single Image","date":"2022-07-22","arxiv_id":"2207.11232","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-forgotten-danger-in-dnn-supervision-testing","title":"Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing","date":"2022-07-21","arxiv_id":"2207.10495","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-based-approach-to","title":"A machine learning based approach to gravitational lens identification with the International LOFAR Telescope","date":"2022-07-21","arxiv_id":"2207.10698","n_code_links":0,"syntology":null},{"paper":null,"slug":"addressing-optimism-bias-in-sequence-modeling","title":"Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning","date":"2022-07-21","arxiv_id":"2207.10295","n_code_links":0,"syntology":null},{"paper":"/paper/autoalignv2-deformable-feature-aggregation","slug":"autoalignv2-deformable-feature-aggregation","title":"AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection","date":"2022-07-21","arxiv_id":"2207.10316","n_code_links":1,"syntology":null},{"paper":null,"slug":"classifying-crop-types-using-gaussian","title":"Classifying Crop Types using Gaussian Bayesian Models and Neural Networks on GHISACONUS USGS data from NASA Hyperspectral Satellite Imagery","date":"2022-07-21","arxiv_id":"2207.11228","n_code_links":0,"syntology":null},{"paper":"/paper/codet-code-generation-with-generated-tests","slug":"codet-code-generation-with-generated-tests","title":"CodeT: Code Generation with Generated Tests","date":"2022-07-21","arxiv_id":"2207.10397","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["microsoft/codet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/data-driven-stochastic-ac-opf-using-gaussian","slug":"data-driven-stochastic-ac-opf-using-gaussian","title":"Data-Driven Stochastic AC-OPF using Gaussian Processes","date":"2022-07-21","arxiv_id":"2207.10781","n_code_links":1,"syntology":null},{"paper":"/paper/domain-generalization-for-activity","slug":"domain-generalization-for-activity","title":"Domain Generalization for Activity Recognition via Adaptive Feature Fusion","date":"2022-07-21","arxiv_id":"2207.11221","n_code_links":1,"syntology":null},{"paper":null,"slug":"error-compensation-framework-for-flow-guided","title":"Error Compensation Framework for Flow-Guided Video Inpainting","date":"2022-07-21","arxiv_id":"2207.10391","n_code_links":0,"syntology":null},{"paper":null,"slug":"land-classification-in-satellite-images-by","title":"Land Classification in Satellite Images by Injecting Traditional Features to CNN Models","date":"2022-07-21","arxiv_id":"2207.10368","n_code_links":0,"syntology":null},{"paper":null,"slug":"metacomp-learning-to-adapt-for-online-depth","title":"MetaComp: Learning to Adapt for Online Depth Completion","date":"2022-07-21","arxiv_id":"2207.10623","n_code_links":0,"syntology":null},{"paper":null,"slug":"mqretnn-multi-horizon-time-series-forecasting","title":"MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation","date":"2022-07-21","arxiv_id":"2207.10517","n_code_links":0,"syntology":null},{"paper":null,"slug":"perspectives-on-distribution-network-flexible","title":"Perspectives on distribution network flexible and curtailable resource activation and needs assessment","date":"2022-07-21","arxiv_id":"2207.10296","n_code_links":0,"syntology":null}],"record_sha256":"885e8d72bd390fbeaa3e55db7e8bf470e3fc7211c223bfeb14f659fa17e4d299","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}