{"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":"/task/feature-selection/papers/2","list_of":"/task/feature-selection","task":"feature selection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":30,"rows_per_page":100,"rows":[101,200],"of":2971,"counts":{"archive_papers_tagged":2971,"with_a_code_link":707,"where_syntology_ran_a_sample":85,"not_listed_spam_title":0,"listed":2971,"listed_where_code_ran":85,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":71,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":71,"listed_every_run_a_failure_of_syntologys_instrument":14,"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":"/task/feature-selection","prev":"/task/feature-selection","next":"/task/feature-selection/papers/3","papers":[{"url":"/paper/failuresensoriq-a-multi-choice-qa-dataset-for","slug":"failuresensoriq-a-multi-choice-qa-dataset-for","title":"FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes","date":"2025-06-03","arxiv_id":"2506.03278","repositories_listed":1,"syntology":null},{"url":"/paper/biological-pathway-guided-gene-selection","slug":"biological-pathway-guided-gene-selection","title":"Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning","date":"2025-05-30","arxiv_id":"2505.24155","repositories_listed":1,"syntology":null},{"url":"/paper/samamba-adaptive-state-space-modeling-with","slug":"samamba-adaptive-state-space-modeling-with","title":"SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection","date":"2025-05-29","arxiv_id":"2505.23214","repositories_listed":1,"syntology":null},{"url":"/paper/dream-drafting-with-refined-target-features","slug":"dream-drafting-with-refined-target-features","title":"DREAM: Drafting with Refined Target Features and Entropy-Adaptive Cross-Attention Fusion for Multimodal Speculative Decoding","date":"2025-05-25","arxiv_id":"2505.19201","repositories_listed":1,"syntology":null},{"url":"/paper/mlran-a-behavioural-dataset-for-ransomware","slug":"mlran-a-behavioural-dataset-for-ransomware","title":"MLRan: A Behavioural Dataset for Ransomware Analysis and Detection","date":"2025-05-24","arxiv_id":"2505.18613","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-in-svm-prediction","slug":"uncertainty-quantification-in-svm-prediction","title":"Uncertainty Quantification in SVM prediction","date":"2025-05-21","arxiv_id":"2505.15429","repositories_listed":1,"syntology":null},{"url":"/paper/from-local-details-to-global-context","slug":"from-local-details-to-global-context","title":"From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection","date":"2025-05-19","arxiv_id":"2505.13233","repositories_listed":1,"syntology":null},{"url":"/paper/hr-vilage-3k3m-a-human-respiratory-viral","slug":"hr-vilage-3k3m-a-human-respiratory-viral","title":"HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity","date":"2025-05-19","arxiv_id":"2505.14725","repositories_listed":1,"syntology":null},{"url":"/paper/bensparx-a-robust-explainable-machine","slug":"bensparx-a-robust-explainable-machine","title":"BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech","date":"2025-05-18","arxiv_id":"2505.12192","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-smiles-notational-inconsistencies","slug":"impact-of-smiles-notational-inconsistencies","title":"Impact of SMILES Notational Inconsistencies on Chemical Language Model Performance","date":"2025-05-11","arxiv_id":"2505.07139","repositories_listed":1,"syntology":null},{"url":"/paper/tacfn-transformer-based-adaptive-cross-modal","slug":"tacfn-transformer-based-adaptive-cross-modal","title":"TACFN: Transformer-based Adaptive Cross-modal Fusion Network for Multimodal Emotion Recognition","date":"2025-05-10","arxiv_id":"2505.06536","repositories_listed":1,"syntology":null},{"url":"/paper/from-pixels-to-perception-interpretable","slug":"from-pixels-to-perception-interpretable","title":"From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection","date":"2025-05-09","arxiv_id":"2505.06003","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-satellite-object-localization-with","slug":"enhancing-satellite-object-localization-with","title":"Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling","date":"2025-05-08","arxiv_id":"2505.05599","repositories_listed":1,"syntology":null},{"url":"/paper/uncovering-population-pk-covariates-from-vae","slug":"uncovering-population-pk-covariates-from-vae","title":"Uncovering Population PK Covariates from VAE-Generated Latent Spaces","date":"2025-05-05","arxiv_id":"2505.02514","repositories_listed":1,"syntology":null},{"url":"/paper/evolutionary-optimization-for-the","slug":"evolutionary-optimization-for-the","title":"Evolutionary Optimization for the Classification of Small Molecules Regulating the Circadian Rhythm Period: A Reliable Assessment","date":"2025-04-27","arxiv_id":"2505.05485","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-subspace-generation-for-outlier","slug":"adversarial-subspace-generation-for-outlier","title":"Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data","date":"2025-04-10","arxiv_id":"2504.07522","repositories_listed":1,"syntology":null},{"url":"/paper/pruning-based-tinyml-optimization-of-machine","slug":"pruning-based-tinyml-optimization-of-machine","title":"Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure","date":"2025-03-19","arxiv_id":"2503.14799","repositories_listed":1,"syntology":null},{"url":"/paper/llm-fe-automated-feature-engineering-for","slug":"llm-fe-automated-feature-engineering-for","title":"LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers","date":"2025-03-18","arxiv_id":"2503.14434","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/llm-fe-automated-feature-engineering-for#ran","syntology_url":"https://syntology.ai/paper/2503.14434","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.14434"}},"official":{"repos":["nikhilsab/llmfe"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/gfsnetwork-differentiable-feature-selection","slug":"gfsnetwork-differentiable-feature-selection","title":"GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid Relaxation","date":"2025-03-17","arxiv_id":"2503.13304","repositories_listed":1,"syntology":null},{"url":"/paper/shufflegate-an-efficient-and-self-polarizing","slug":"shufflegate-an-efficient-and-self-polarizing","title":"ShuffleGate: An Efficient and Self-Polarizing Feature Selection Method for Large-Scale Deep Models in Industry","date":"2025-03-12","arxiv_id":"2503.09315","repositories_listed":1,"syntology":null},{"url":"/paper/shap-integrated-convolutional-diagnostic","slug":"shap-integrated-convolutional-diagnostic","title":"SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis","date":"2025-03-10","arxiv_id":"2503.08712","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multimodal-learning-for-ophthalmic","slug":"robust-multimodal-learning-for-ophthalmic","title":"Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation","date":"2025-03-07","arxiv_id":"2503.05319","repositories_listed":1,"syntology":null},{"url":"/paper/tuning-free-structured-sparse-pca-via-deep","slug":"tuning-free-structured-sparse-pca-via-deep","title":"Tuning-Free Structured Sparse PCA via Deep Unfolding Networks","date":"2025-02-28","arxiv_id":"2502.20837","repositories_listed":1,"syntology":null},{"url":"/paper/automl-for-multi-class-anomaly-compensation","slug":"automl-for-multi-class-anomaly-compensation","title":"AutoML for Multi-Class Anomaly Compensation of Sensor Drift","date":"2025-02-26","arxiv_id":"2502.19180","repositories_listed":1,"syntology":null},{"url":"/paper/offload-rethinking-by-cloud-assistance-for","slug":"offload-rethinking-by-cloud-assistance-for","title":"Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs","date":"2025-02-21","arxiv_id":"2502.15285","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-autoencoder-features-for","slug":"sparse-autoencoder-features-for","title":"Sparse Autoencoder Features for Classifications and Transferability","date":"2025-02-17","arxiv_id":"2502.11367","repositories_listed":1,"syntology":null},{"url":"/paper/llm-lasso-a-robust-framework-for-domain","slug":"llm-lasso-a-robust-framework-for-domain","title":"LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization","date":"2025-02-15","arxiv_id":"2502.10648","repositories_listed":1,"syntology":null},{"url":"/paper/symantic-an-efficient-symbolic-regression","slug":"symantic-an-efficient-symbolic-regression","title":"SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond","date":"2025-02-05","arxiv_id":"2502.03367","repositories_listed":1,"syntology":null},{"url":"/paper/gbfrs-robust-fuzzy-rough-sets-via-granular","slug":"gbfrs-robust-fuzzy-rough-sets-via-granular","title":"GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing","date":"2025-01-30","arxiv_id":"2501.18413","repositories_listed":1,"syntology":null},{"url":"/paper/saeuron-interpretable-concept-unlearning-in","slug":"saeuron-interpretable-concept-unlearning-in","title":"SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders","date":"2025-01-29","arxiv_id":"2501.18052","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_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","sample_list":"/paper/saeuron-interpretable-concept-unlearning-in#ran","syntology_url":"https://syntology.ai/paper/2501.18052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.18052"}},"official":{"repos":["cywinski/saeuron"],"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"]}}},{"url":"/paper/finding-reproducible-and-prognostic-radiomic","slug":"finding-reproducible-and-prognostic-radiomic","title":"Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases","date":"2025-01-20","arxiv_id":"2501.11221","repositories_listed":1,"syntology":null},{"url":"/paper/multitask-auxiliary-network-for-perceptual","slug":"multitask-auxiliary-network-for-perceptual","title":"Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images","date":"2025-01-20","arxiv_id":"2501.11512","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-inference-for-sequential-feature","slug":"statistical-inference-for-sequential-feature","title":"Statistical Inference for Sequential Feature Selection after Domain Adaptation","date":"2025-01-17","arxiv_id":"2501.09933","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-llm-abilities-to-understand","slug":"evaluating-llm-abilities-to-understand","title":"Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval","date":"2025-01-16","arxiv_id":"2501.09384","repositories_listed":1,"syntology":null},{"url":"/paper/metrics-for-inter-dataset-similarity-with","slug":"metrics-for-inter-dataset-similarity-with","title":"Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version","date":"2025-01-16","arxiv_id":"2501.09591","repositories_listed":1,"syntology":null},{"url":"/paper/scoring-with-large-language-models-a-study-on","slug":"scoring-with-large-language-models-a-study-on","title":"Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues","date":"2024-12-28","arxiv_id":"2412.20264","repositories_listed":1,"syntology":null},{"url":"/paper/bi-sparse-unsupervised-feature-selection","slug":"bi-sparse-unsupervised-feature-selection","title":"Bi-Sparse Unsupervised Feature Selection","date":"2024-12-22","arxiv_id":"2412.16819","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-feature-exclusion-ranking-for-deep","slug":"iterative-feature-exclusion-ranking-for-deep","title":"Iterative Feature Exclusion Ranking for Deep Tabular Learning","date":"2024-12-21","arxiv_id":"2412.16442","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-factor-market-neutral-investment","slug":"a-multi-factor-market-neutral-investment","title":"A multi-factor market-neutral investment strategy for New York Stock Exchange equities","date":"2024-12-16","arxiv_id":"2412.12350","repositories_listed":1,"syntology":null},{"url":"/paper/biological-and-radiological-dictionary-of","slug":"biological-and-radiological-dictionary-of","title":"Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0","date":"2024-12-14","arxiv_id":"2412.10967","repositories_listed":1,"syntology":null},{"url":"/paper/one-node-one-model-featuring-the-missing-half","slug":"one-node-one-model-featuring-the-missing-half","title":"One Node One Model: Featuring the Missing-Half for Graph Clustering","date":"2024-12-13","arxiv_id":"2412.09902","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":7,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/one-node-one-model-featuring-the-missing-half#ran","syntology_url":"https://syntology.ai/paper/2412.09902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.09902"}},"official":{"repos":["xiexuanting/fpgc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/delay-estimation-based-on-multiple-stage","slug":"delay-estimation-based-on-multiple-stage","title":"Delay Estimation Based on Multiple Stage Message Passing With Attention Mechanism Using a Real Network Communication Dataset","date":"2024-12-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pdg2seq-periodic-dynamic-graph-to-sequence","slug":"pdg2seq-periodic-dynamic-graph-to-sequence","title":"PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction","date":"2024-12-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explainable-fault-and-severity-classification","slug":"explainable-fault-and-severity-classification","title":"Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks","date":"2024-12-02","arxiv_id":"2412.01322","repositories_listed":1,"syntology":null},{"url":"/paper/training-a-neural-netwok-for-data-reduction","slug":"training-a-neural-netwok-for-data-reduction","title":"Training a neural netwok for data reduction and better generalization","date":"2024-11-26","arxiv_id":"2411.17180","repositories_listed":1,"syntology":null},{"url":"/paper/online-high-frequency-trading-stock","slug":"online-high-frequency-trading-stock","title":"Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks","date":"2024-11-23","arxiv_id":"2412.16160","repositories_listed":1,"syntology":null},{"url":"/paper/d-cube-exploiting-hyper-features-of-diffusion","slug":"d-cube-exploiting-hyper-features-of-diffusion","title":"D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification","date":"2024-11-17","arxiv_id":"2411.11087","repositories_listed":1,"syntology":null},{"url":"/paper/random-feature-baselines-provide","slug":"random-feature-baselines-provide","title":"Random feature baselines provide distributional performance and feature selection benchmarks for clinical and 'omic machine learning","date":"2024-11-15","arxiv_id":"2411.10574","repositories_listed":1,"syntology":null},{"url":"/paper/gemid-generalizable-models-for-iot-device","slug":"gemid-generalizable-models-for-iot-device","title":"GeMID: Generalizable Models for IoT Device Identification","date":"2024-11-05","arxiv_id":"2411.14441","repositories_listed":1,"syntology":null},{"url":"/paper/towards-robust-text-classification-mitigating","slug":"towards-robust-text-classification-mitigating","title":"Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective","date":"2024-11-01","arxiv_id":"2411.01045","repositories_listed":1,"syntology":null},{"url":"/paper/xai-based-feature-selection-for-improved","slug":"xai-based-feature-selection-for-improved","title":"XAI-based Feature Selection for Improved Network Intrusion Detection Systems","date":"2024-10-14","arxiv_id":"2410.10050","repositories_listed":1,"syntology":null},{"url":"/paper/robust-ai-generated-text-detection-by","slug":"robust-ai-generated-text-detection-by","title":"Robust AI-Generated Text Detection by Restricted Embeddings","date":"2024-10-10","arxiv_id":"2410.08113","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/robust-ai-generated-text-detection-by#ran","syntology_url":"https://syntology.ai/paper/2410.08113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08113"}},"official":{"repos":["silversolver/robustatd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/shap-select-lightweight-feature-selection","slug":"shap-select-lightweight-feature-selection","title":"Shap-Select: Lightweight Feature Selection Using SHAP Values and Regression","date":"2024-10-09","arxiv_id":"2410.06815","repositories_listed":1,"syntology":null},{"url":"/paper/mdap-a-multi-view-disentangled-and-adaptive","slug":"mdap-a-multi-view-disentangled-and-adaptive","title":"MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation","date":"2024-10-08","arxiv_id":"2410.05877","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/mdap-a-multi-view-disentangled-and-adaptive#ran","syntology_url":"https://syntology.ai/paper/2410.05877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.05877"}},"official":{"repos":["the-garden-of-sinner/mdap"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/comparative-analysis-of-multi-omics","slug":"comparative-analysis-of-multi-omics","title":"Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification","date":"2024-10-05","arxiv_id":"2410.05325","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-diffusion-model-activations-have-been","slug":"not-all-diffusion-model-activations-have-been","title":"Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features","date":"2024-10-04","arxiv_id":"2410.03558","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/not-all-diffusion-model-activations-have-been#ran","syntology_url":"https://syntology.ai/paper/2410.03558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.03558"}},"official":{"repos":["darkbblue/generic-diffusion-feature"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-driven-feature-selection-and","slug":"knowledge-driven-feature-selection-and","title":"Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models","date":"2024-10-02","arxiv_id":"2410.01795","repositories_listed":1,"syntology":null},{"url":"/paper/interval-estimation-of-coefficients-in","slug":"interval-estimation-of-coefficients-in","title":"Interval Estimation of Coefficients in Penalized Regression Models of Insurance Data","date":"2024-10-01","arxiv_id":"2410.01008","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-multivariate-time-series-based","slug":"enhancing-multivariate-time-series-based","title":"Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning","date":"2024-09-21","arxiv_id":"2409.14016","repositories_listed":1,"syntology":null},{"url":"/paper/coxkan-kolmogorov-arnold-networks-for","slug":"coxkan-kolmogorov-arnold-networks-for","title":"CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis","date":"2024-09-06","arxiv_id":"2409.04290","repositories_listed":1,"syntology":null},{"url":"/paper/towards-autonomous-cybersecurity-an","slug":"towards-autonomous-cybersecurity-an","title":"Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection","date":"2024-09-05","arxiv_id":"2409.03141","repositories_listed":1,"syntology":null},{"url":"/paper/heartbeat-classification-using-various","slug":"heartbeat-classification-using-various","title":"Heartbeat classification using various machine learning models: A comparative study","date":"2024-09-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lasso-mogat-a-multi-omics-graph-attention","slug":"lasso-mogat-a-multi-omics-graph-attention","title":"LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification","date":"2024-08-30","arxiv_id":"2408.17384","repositories_listed":1,"syntology":null},{"url":"/paper/mamba2mil-state-space-duality-based-multiple","slug":"mamba2mil-state-space-duality-based-multiple","title":"Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology","date":"2024-08-27","arxiv_id":"2408.15032","repositories_listed":1,"syntology":null},{"url":"/paper/fsdem-feature-selection-dynamic-evaluation","slug":"fsdem-feature-selection-dynamic-evaluation","title":"FSDEM: Feature Selection Dynamic Evaluation Metric","date":"2024-08-26","arxiv_id":"2408.14234","repositories_listed":1,"syntology":null},{"url":"/paper/classification-of-mitral-regurgitation-from","slug":"classification-of-mitral-regurgitation-from","title":"Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features","date":"2024-08-21","arxiv_id":"2408.11532","repositories_listed":1,"syntology":null},{"url":"/paper/approximation-of-the-proximal-operator-of-the","slug":"approximation-of-the-proximal-operator-of-the","title":"Approximation of the Proximal Operator of the $\\ell_\\infty$ Norm Using a Neural Network","date":"2024-08-20","arxiv_id":"2408.11211","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-aware-streaming-feature-selection","slug":"fairness-aware-streaming-feature-selection","title":"Fairness-Aware Streaming Feature Selection with Causal Graphs","date":"2024-08-17","arxiv_id":"2408.12665","repositories_listed":1,"syntology":null},{"url":"/paper/impact-of-comprehensive-data-preprocessing-on","slug":"impact-of-comprehensive-data-preprocessing-on","title":"Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality","date":"2024-08-15","arxiv_id":"2408.08142","repositories_listed":1,"syntology":null},{"url":"/paper/information-theoretic-measures-on-lattices","slug":"information-theoretic-measures-on-lattices","title":"Information-Theoretic Measures on Lattices for High-Order Interactions","date":"2024-08-14","arxiv_id":"2408.07533","repositories_listed":1,"syntology":null},{"url":"/paper/perspectives-comparison-of-deep-learning","slug":"perspectives-comparison-of-deep-learning","title":"Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data","date":"2024-08-14","arxiv_id":"2408.07786","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-the-power-of-sparse-neural-networks","slug":"unveiling-the-power-of-sparse-neural-networks","title":"Unveiling the Power of Sparse Neural Networks for Feature Selection","date":"2024-08-08","arxiv_id":"2408.04583","repositories_listed":1,"syntology":null},{"url":"/paper/2408-02845","slug":"2408-02845","title":"Heterogeneous graph attention network improves cancer multiomics integration","date":"2024-08-05","arxiv_id":"2408.02845","repositories_listed":1,"syntology":null},{"url":"/paper/practical-video-object-detection-via-feature","slug":"practical-video-object-detection-via-feature","title":"Practical Video Object Detection via Feature Selection and Aggregation","date":"2024-07-29","arxiv_id":"2407.19650","repositories_listed":1,"syntology":null},{"url":"/paper/comprehensive-attribution-inherently","slug":"comprehensive-attribution-inherently","title":"Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector","date":"2024-07-27","arxiv_id":"2407.19308","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/comprehensive-attribution-inherently#ran","syntology_url":"https://syntology.ai/paper/2407.19308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.19308"}},"official":{"repos":["zood123/comet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-tensor-pca-via-tensor-decomposition","slug":"sparse-tensor-pca-via-tensor-decomposition","title":"Sparse Tensor PCA via Tensor Decomposition for Unsupervised Feature Selection","date":"2024-07-24","arxiv_id":"2407.16985","repositories_listed":1,"syntology":null},{"url":"/paper/distance-based-mutual-congestion-feature","slug":"distance-based-mutual-congestion-feature","title":"Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets","date":"2024-07-22","arxiv_id":"2407.15611","repositories_listed":1,"syntology":null},{"url":"/paper/global-local-similarity-for-efficient-fine","slug":"global-local-similarity-for-efficient-fine","title":"Global-Local Similarity for Efficient Fine-Grained Image Recognition with Vision Transformers","date":"2024-07-17","arxiv_id":"2407.12891","repositories_listed":1,"syntology":null},{"url":"/paper/word-embedding-dimension-reduction-via-weakly","slug":"word-embedding-dimension-reduction-via-weakly","title":"Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection","date":"2024-07-17","arxiv_id":"2407.12342","repositories_listed":1,"syntology":null},{"url":"/paper/local-feature-selection-without-label-or","slug":"local-feature-selection-without-label-or","title":"Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions","date":"2024-07-16","arxiv_id":"2407.11778","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/local-feature-selection-without-label-or#ran","syntology_url":"https://syntology.ai/paper/2407.11778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11778"}},"official":{"repos":["garfieldlyu/suwr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/thyroidiomics-an-automated-pipeline-for","slug":"thyroidiomics-an-automated-pipeline-for","title":"Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images","date":"2024-07-14","arxiv_id":"2407.10336","repositories_listed":1,"syntology":null},{"url":"/paper/brainmetdetect-predicting-primary-tumor-from","slug":"brainmetdetect-predicting-primary-tumor-from","title":"BrainMetDetect: Predicting Primary Tumor from Brain Metastasis MRI Data Using Radiomic Features and Machine Learning Algorithms","date":"2024-07-06","arxiv_id":"2407.05051","repositories_listed":1,"syntology":null},{"url":"/paper/hard-attention-gates-with-gradient-routing","slug":"hard-attention-gates-with-gradient-routing","title":"Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing","date":"2024-07-05","arxiv_id":"2407.04400","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-test-for-data-analysis-pipeline","slug":"statistical-test-for-data-analysis-pipeline","title":"Statistical Test for Feature Selection Pipelines by Selective Inference","date":"2024-06-27","arxiv_id":"2406.18902","repositories_listed":1,"syntology":null},{"url":"/paper/chaosmining-a-benchmark-to-evaluate-post-hoc","slug":"chaosmining-a-benchmark-to-evaluate-post-hoc","title":"ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments","date":"2024-06-17","arxiv_id":"2406.12150","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/chaosmining-a-benchmark-to-evaluate-post-hoc#ran","syntology_url":"https://syntology.ai/paper/2406.12150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12150"}},"official":{"repos":["geshijoker/chaosmining"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/optimized-feature-generation-for-tabular-data","slug":"optimized-feature-generation-for-tabular-data","title":"Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning","date":"2024-06-12","arxiv_id":"2406.08527","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimized-feature-generation-for-tabular-data#ran","syntology_url":"https://syntology.ai/paper/2406.08527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.08527"}},"official":{"repos":["jaehyun513/octree"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/giving-each-task-what-it-needs-leveraging","slug":"giving-each-task-what-it-needs-leveraging","title":"Giving each task what it needs -- leveraging structured sparsity for tailored multi-task learning","date":"2024-06-05","arxiv_id":"2406.03048","repositories_listed":1,"syntology":null},{"url":"/paper/interpretabnet-distilling-predictive-signals","slug":"interpretabnet-distilling-predictive-signals","title":"InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation","date":"2024-06-01","arxiv_id":"2406.00426","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":10,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 10 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) · 5 unverified; every one of the 10 samples that ran constructed an object rather than computing a result","sample_list":"/paper/interpretabnet-distilling-predictive-signals#ran","syntology_url":"https://syntology.ai/paper/2406.00426","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00426"}},"official":{"repos":["jacobyhsi/InterpreTabNet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":10,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/analysis-of-clinical-dosimetric-and-radiomic","slug":"analysis-of-clinical-dosimetric-and-radiomic","title":"Analysis of clinical, dosimetric and radiomic features for predicting local failure after stereotactic radiotherapy of brain metastases in malignant melanoma","date":"2024-05-31","arxiv_id":"2405.20825","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-feature-boosting-for-explainable-1","slug":"iterative-feature-boosting-for-explainable-1","title":"Iterative Feature Boosting for Explainable Speech Emotion Recognition","date":"2024-05-30","arxiv_id":"2405.20172","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-recursive-feature-elimination","slug":"conformal-recursive-feature-elimination","title":"Conformal Recursive Feature Elimination","date":"2024-05-29","arxiv_id":"2405.19429","repositories_listed":1,"syntology":null},{"url":"/paper/partial-information-decomposition-for-data","slug":"partial-information-decomposition-for-data","title":"Partial Information Decomposition for Data Interpretability and Feature Selection","date":"2024-05-29","arxiv_id":"2405.19212","repositories_listed":1,"syntology":null},{"url":"/paper/decomposing-the-neurons-activation-sparsity","slug":"decomposing-the-neurons-activation-sparsity","title":"Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation","date":"2024-05-26","arxiv_id":"2405.16486","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/decomposing-the-neurons-activation-sparsity#ran","syntology_url":"https://syntology.ai/paper/2405.16486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16486"}},"official":{"repos":["royzry98/moase-pytorch"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/depth-linear-discrimination-oriented-feature","slug":"depth-linear-discrimination-oriented-feature","title":"Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction","date":"2024-05-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mtlcomb-multi-task-learning-combining","slug":"mtlcomb-multi-task-learning-combining","title":"MTLComb: multi-task learning combining regression and classification tasks for joint feature selection","date":"2024-05-16","arxiv_id":"2405.09886","repositories_listed":1,"syntology":null},{"url":"/paper/an-interpretable-adaptive-multiscale","slug":"an-interpretable-adaptive-multiscale","title":"An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular Data","date":"2024-05-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/is-interpretable-machine-learning-effective","slug":"is-interpretable-machine-learning-effective","title":"Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?","date":"2024-05-13","arxiv_id":"2405.07782","repositories_listed":1,"syntology":null},{"url":"/paper/decoding-cognitive-health-using-machine","slug":"decoding-cognitive-health-using-machine","title":"Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern","date":"2024-05-11","arxiv_id":"2405.07070","repositories_listed":1,"syntology":null},{"url":"/paper/fingertip-video-dataset-for-non-invasive","slug":"fingertip-video-dataset-for-non-invasive","title":"Fingertip Video Dataset for Non-Invasive Diagnosis of Anemia Using ResNet-18 Classifier","date":"2024-05-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/guiding-adaptive-shrinkage-by-co-data-to","slug":"guiding-adaptive-shrinkage-by-co-data-to","title":"Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection","date":"2024-05-08","arxiv_id":"2405.04917","repositories_listed":1,"syntology":null}],"record_sha256":"b667171e468888aff23425e0ea363343a689841612335ee1df653c086c276dce","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}