{"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/active-learning/papers/11","list_of":"/task/active-learning","task":"Active Learning","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":11,"pages_in_order":31,"rows_per_page":100,"rows":[1001,1100],"of":3073,"counts":{"archive_papers_tagged":3073,"with_a_code_link":913,"where_syntology_ran_a_sample":195,"not_listed_spam_title":0,"listed":3073,"listed_where_code_ran":195,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":164,"every_run_a_failure_of_syntologys_instrument":31,"listed_with_a_run_with_no_instrument_failure":164,"listed_every_run_a_failure_of_syntologys_instrument":31,"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/active-learning","prev":"/task/active-learning/papers/10","next":"/task/active-learning/papers/12","papers":[{"url":null,"slug":"bayesian-active-learning-for-multi-criteria","title":"Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment","date":"2025-03-01","arxiv_id":"2503.00479","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-conditional-inverse","title":"Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models","date":"2025-02-24","arxiv_id":"2502.16984","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-llms-to-active-learning-towards-cost","title":"Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data","date":"2025-02-24","arxiv_id":"2502.16892","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributionally-robust-active-learning-for","title":"Distributionally Robust Active Learning for Gaussian Process Regression","date":"2025-02-24","arxiv_id":"2502.16870","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-classification-from-a-signal","title":"Active Learning Classification from a Signal Separation Perspective","date":"2025-02-23","arxiv_id":"2502.16425","repositories_listed":0,"syntology":null},{"url":null,"slug":"autotandemml-active-learning-enhanced-tandem","title":"AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems","date":"2025-02-21","arxiv_id":"2502.15643","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-selection-to-generation-a-survey-of-llm","title":"From Selection to Generation: A Survey of LLM-based Active Learning","date":"2025-02-17","arxiv_id":"2502.11767","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-gaussian-process","title":"Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design","date":"2025-02-17","arxiv_id":"2502.11369","repositories_listed":0,"syntology":null},{"url":null,"slug":"co-designing-large-language-model-tools-for","title":"Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators","date":"2025-02-13","arxiv_id":"2502.09799","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-rag-with-active-learning-on","title":"Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables","date":"2025-02-13","arxiv_id":"2502.09073","repositories_listed":0,"syntology":null},{"url":null,"slug":"activessf-an-active-learning-guided-self","title":"ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification","date":"2025-02-12","arxiv_id":"2502.08200","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifidelity-simulation-based-inference-for","title":"Multifidelity Simulation-based Inference for Computationally Expensive Simulators","date":"2025-02-12","arxiv_id":"2502.08416","repositories_listed":0,"syntology":null},{"url":null,"slug":"educating-a-responsible-ai-workforce-piloting","title":"Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course","date":"2025-02-11","arxiv_id":"2502.07931","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-foundation-model-for-physics","title":"Towards a Foundation Model for Physics-Informed Neural Networks: Multi-PDE Learning with Active Sampling","date":"2025-02-11","arxiv_id":"2502.07425","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-physics-based-data-driven-model-for-co-2","title":"A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories","date":"2025-02-10","arxiv_id":"2502.06323","repositories_listed":0,"syntology":null},{"url":null,"slug":"al-pinn-active-learning-driven-physics","title":"AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations","date":"2025-02-06","arxiv_id":"2502.03963","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-quantification-of-breast-cancer","title":"Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation","date":"2025-02-06","arxiv_id":"2502.04083","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-unstructured-medical-texts-with","title":"Mining Unstructured Medical Texts With Conformal Active Learning","date":"2025-02-05","arxiv_id":"2502.04372","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-based-experimental","title":"Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics","date":"2025-02-03","arxiv_id":"2502.01012","repositories_listed":0,"syntology":null},{"url":null,"slug":"omni-mol-exploring-universal-convergent-space","title":"Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks","date":"2025-02-03","arxiv_id":"2502.01074","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-semi-supervised-and-active","title":"Integrating Semi-Supervised and Active Learning for Semantic Segmentation","date":"2025-01-31","arxiv_id":"2501.19227","repositories_listed":0,"syntology":null},{"url":null,"slug":"amortized-safe-active-learning-for-real-time","title":"Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions","date":"2025-01-26","arxiv_id":"2501.15458","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-the-ssl-al-barrier-a-synergistic","title":"Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection","date":"2025-01-26","arxiv_id":"2501.15449","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-continual-learning","title":"Active Learning for Continual Learning: Keeping the Past Alive in the Present","date":"2025-01-24","arxiv_id":"2501.14278","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-based-adaptive-tracking","title":"Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles","date":"2025-01-24","arxiv_id":"2501.14672","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-interpretable-deep-learning-framework","title":"Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI","date":"2025-01-24","arxiv_id":"2501.14885","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-auto-labeling-of-large-scale","title":"Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach","date":"2025-01-18","arxiv_id":"2501.10809","repositories_listed":0,"syntology":null},{"url":null,"slug":"big-batch-bayesian-active-learning-by","title":"Big Batch Bayesian Active Learning by Considering Predictive Probabilities","date":"2025-01-14","arxiv_id":"2501.08223","repositories_listed":0,"syntology":null},{"url":null,"slug":"mechanics-and-design-of-metastructured","title":"Mechanics and Design of Metastructured Auxetic Patches with Bio-inspired Materials","date":"2025-01-08","arxiv_id":"2501.06233","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-tutorial-label-efficient-two-sample","title":"Advanced Tutorial: Label-Efficient Two-Sample Tests","date":"2025-01-07","arxiv_id":"2501.03568","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-enables-extrapolation-in","title":"Active Learning Enables Extrapolation in Molecular Generative Models","date":"2025-01-03","arxiv_id":"2501.02059","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-out-of-distribution-filtering-and-data","title":"Joint Out-of-Distribution Filtering and Data Discovery Active Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-cost-effective-learning-a-synergy-of","title":"Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"u-gift-uncertainty-guided-firewall-for-toxic","title":"U-GIFT: Uncertainty-Guided Firewall for Toxic Speech in Few-Shot Scenario","date":"2025-01-01","arxiv_id":"2501.00907","repositories_listed":0,"syntology":null},{"url":null,"slug":"acil-active-class-incremental-learning-for","title":"ACIL: Active Class Incremental Learning for Image Classification","date":"2024-12-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-of-general-halfspaces-label","title":"Active Learning of General Halfspaces: Label Queries vs Membership Queries","date":"2024-12-31","arxiv_id":"2501.00508","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-human-in-the-loop-active-learning-a","title":"Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems","date":"2024-12-31","arxiv_id":"2501.00277","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-with-variational-quantum","title":"Active Learning with Variational Quantum Circuits for Quantum Process Tomography","date":"2024-12-30","arxiv_id":"2412.20925","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-herding-one-active-learning","title":"Uncertainty Herding: One Active Learning Method for All Label Budgets","date":"2024-12-30","arxiv_id":"2412.20644","repositories_listed":0,"syntology":null},{"url":null,"slug":"staykate-hybrid-in-context-example-selection","title":"STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains","date":"2024-12-28","arxiv_id":"2412.20043","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-with-deep-reinforcement","title":"Image Classification with Deep Reinforcement Active Learning","date":"2024-12-27","arxiv_id":"2412.19877","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-continual-open","title":"Uncertainty Quantification in Continual Open-World Learning","date":"2024-12-21","arxiv_id":"2412.16409","repositories_listed":0,"syntology":null},{"url":null,"slug":"function-space-diversity-for-uncertainty","title":"Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles","date":"2024-12-20","arxiv_id":"2412.15758","repositories_listed":0,"syntology":null},{"url":null,"slug":"galot-generative-active-learning-via","title":"GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation","date":"2024-12-18","arxiv_id":"2412.16227","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-reinforcement-learning-strategies-for","title":"Active Reinforcement Learning Strategies for Offline Policy Improvement","date":"2024-12-17","arxiv_id":"2412.13106","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoscilab-a-self-driving-laboratory-for","title":"AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery","date":"2024-12-16","arxiv_id":"2412.12347","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-large-language-model-based-knowledge","title":"Active Large Language Model-based Knowledge Distillation for Session-based Recommendation","date":"2024-12-15","arxiv_id":"2502.15685","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-parameter-learning-approach-to-the","title":"An Active Parameter Learning Approach to The Identification of Safe Regions","date":"2024-12-14","arxiv_id":"2412.10627","repositories_listed":0,"syntology":null},{"url":null,"slug":"congruence-based-learning-of-probabilistic","title":"Congruence-based Learning of Probabilistic Deterministic Finite Automata","date":"2024-12-12","arxiv_id":"2412.09760","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-modality-representation-and","title":"Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning","date":"2024-12-12","arxiv_id":"2412.09126","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-active-learning-for-gaussian","title":"Safe Active Learning for Gaussian Differential Equations","date":"2024-12-12","arxiv_id":"2412.09053","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-cost-of-replicability-in-active-learning","title":"The Cost of Replicability in Active Learning","date":"2024-12-12","arxiv_id":"2412.09686","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-select-slices-for-annotation-to-train","title":"How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?","date":"2024-12-11","arxiv_id":"2412.08081","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-active-learning-with-a-bayesian","title":"Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty","date":"2024-12-11","arxiv_id":"2412.08225","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-distribution-learning-using-the-squared","title":"Label Distribution Learning using the Squared Neural Family on the Probability Simplex","date":"2024-12-10","arxiv_id":"2412.07324","repositories_listed":0,"syntology":null},{"url":null,"slug":"maple-a-framework-for-active-preference","title":"MAPLE: A Framework for Active Preference Learning Guided by Large Language Models","date":"2024-12-10","arxiv_id":"2412.07207","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-prediction-uncertainty-of","title":"Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data","date":"2024-12-10","arxiv_id":"2412.07520","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-with-context-sampling-and-one","title":"Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation","date":"2024-12-09","arxiv_id":"2412.06470","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-by-teaching-with-chatgpt-the-effect","title":"Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education","date":"2024-12-05","arxiv_id":"2412.15226","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-layer-privacy-preserving-record-linkage","title":"Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure","date":"2024-12-05","arxiv_id":"2412.04178","repositories_listed":0,"syntology":null},{"url":null,"slug":"superposition-through-active-learning-lens","title":"Superposition through Active Learning lens","date":"2024-12-05","arxiv_id":"2412.16168","repositories_listed":0,"syntology":null},{"url":"/paper/active-learning-of-neural-population-dynamics","slug":"active-learning-of-neural-population-dynamics","title":"Active learning of neural population dynamics using two-photon holographic optogenetics","date":"2024-12-03","arxiv_id":"2412.02529","repositories_listed":0,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":15,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/active-learning-of-neural-population-dynamics#ran","syntology_url":"https://syntology.ai/paper/2412.02529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.02529"}},"official":null}},{"url":null,"slug":"sample-efficient-robot-learning-in-supervised","title":"Sample Efficient Robot Learning in Supervised Effect Prediction Tasks","date":"2024-12-03","arxiv_id":"2412.02331","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-task-inconsistency-based-active","title":"Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition","date":"2024-12-02","arxiv_id":"2412.01171","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-window-decoder-for-sc-ldpc-codes","title":"Neural Window Decoder for SC-LDPC Codes","date":"2024-11-28","arxiv_id":"2411.19092","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-partitioning-inverting-the-paradigm-of","title":"Active partitioning: inverting the paradigm of active learning","date":"2024-11-27","arxiv_id":"2411.18254","repositories_listed":0,"syntology":null},{"url":null,"slug":"oris-online-active-learning-using","title":"ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System","date":"2024-11-27","arxiv_id":"2411.18060","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximally-separated-active-learning","title":"Maximally Separated Active Learning","date":"2024-11-26","arxiv_id":"2411.17444","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-bayesian-uncertainty","title":"A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation","date":"2024-11-25","arxiv_id":"2411.16370","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-active-learning-for-nilm","title":"Benchmarking Active Learning for NILM","date":"2024-11-24","arxiv_id":"2411.15805","repositories_listed":0,"syntology":null},{"url":null,"slug":"influence-functions-and-regularity-tangents","title":"Influence functions and regularity tangents for efficient active learning","date":"2024-11-22","arxiv_id":"2411.15292","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-based-optimization-of-1","title":"Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage","date":"2024-11-21","arxiv_id":"2411.14618","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-active-learning-and-mcmc","title":"Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties","date":"2024-11-20","arxiv_id":"2411.13361","repositories_listed":0,"syntology":null},{"url":null,"slug":"stream-based-active-learning-for-process","title":"Stream-Based Active Learning for Process Monitoring","date":"2024-11-19","arxiv_id":"2411.12563","repositories_listed":0,"syntology":null},{"url":"/paper/molparser-end-to-end-visual-recognition-of","slug":"molparser-end-to-end-visual-recognition-of","title":"MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild","date":"2024-11-17","arxiv_id":"2411.11098","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-quantitative-automata-modulo","title":"Learning Quantitative Automata Modulo Theories","date":"2024-11-15","arxiv_id":"2411.10601","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-in-the-open-world","title":"Deep Active Learning in the Open World","date":"2024-11-10","arxiv_id":"2411.06353","repositories_listed":0,"syntology":null},{"url":null,"slug":"gci-vital-gradual-confidence-improvement-with","title":"GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise","date":"2024-11-08","arxiv_id":"2411.05939","repositories_listed":0,"syntology":null},{"url":null,"slug":"hands-on-tutorial-labeling-with-llm-and-human","title":"Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop","date":"2024-11-07","arxiv_id":"2411.04637","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-guided-llm-knowledge-distillation","title":"Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale","date":"2024-11-07","arxiv_id":"2411.05045","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-matching-approach-to-optimal","title":"An information-matching approach to optimal experimental design and active learning","date":"2024-11-05","arxiv_id":"2411.02740","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-contextual-uncertainty-of-visual","title":"Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models","date":"2024-11-04","arxiv_id":"2411.01925","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-aware-query-policies-in-active-learning","title":"Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration","date":"2024-10-31","arxiv_id":"2411.00137","repositories_listed":0,"syntology":null},{"url":null,"slug":"spiroactive-active-learning-for-efficient","title":"SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry","date":"2024-10-30","arxiv_id":"2410.22950","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-vision-language-models","title":"Active Learning for Vision-Language Models","date":"2024-10-29","arxiv_id":"2410.22187","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-uncertainty-estimations-for","title":"Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials","date":"2024-10-27","arxiv_id":"2410.20398","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-efficiency-identifying-hard","title":"Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits","date":"2024-10-26","arxiv_id":"2410.20041","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-biological-data-acquisition-through","title":"Efficient Biological Data Acquisition through Inference Set Design","date":"2024-10-25","arxiv_id":"2410.19631","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-universe-with-snad-anomaly","title":"Exploring the Universe with SNAD: Anomaly Detection in Astronomy","date":"2024-10-24","arxiv_id":"2410.18875","repositories_listed":0,"syntology":null},{"url":null,"slug":"perturbation-based-graph-active-learning-for","title":"Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning","date":"2024-10-24","arxiv_id":"2410.19176","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-error-correlations-in-evidential","title":"Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation","date":"2024-10-24","arxiv_id":"2410.18461","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-for-robust-robotic","title":"Bayesian optimization for robust robotic grasping using a sensorized compliant hand","date":"2024-10-23","arxiv_id":"2410.18237","repositories_listed":0,"syntology":null},{"url":null,"slug":"regal-python-package-for-active-learning-of","title":"regAL: Python Package for Active Learning of Regression Problems","date":"2024-10-23","arxiv_id":"2410.17917","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-with-manifold-preserving","title":"Deep Active Learning with Manifold-preserving Trajectory Sampling","date":"2024-10-21","arxiv_id":"2410.15605","repositories_listed":0,"syntology":null},{"url":null,"slug":"increasing-interpretability-of-neural","title":"Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency","date":"2024-10-21","arxiv_id":"2410.16115","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-signals-defined-on-graphs-with","title":"Learning signals defined on graphs with optimal transport and Gaussian process regression","date":"2024-10-21","arxiv_id":"2410.15721","repositories_listed":0,"syntology":null},{"url":null,"slug":"coherence-driven-multimodal-safety-dialogue","title":"Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents","date":"2024-10-18","arxiv_id":"2410.14141","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simplifying-and-learnable-graph","title":"A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment","date":"2024-10-17","arxiv_id":"2410.13263","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-learning-framework-for-inclusive","title":"An Active Learning Framework for Inclusive Generation by Large Language Models","date":"2024-10-17","arxiv_id":"2410.13641","repositories_listed":0,"syntology":null},{"url":null,"slug":"railway-lidar-semantic-segmentation-based-on","title":"Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation","date":"2024-10-17","arxiv_id":"2410.13383","repositories_listed":0,"syntology":null}],"record_sha256":"c38716475a34a02de3d8006e151d9513fdd2ced0b96cc99204ae20015a806f7d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}