{"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/sensitivity/papers/11","list_of":"/task/sensitivity","task":"Sensitivity","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":21,"rows_per_page":100,"rows":[1001,1100],"of":2016,"counts":{"archive_papers_tagged":2016,"with_a_code_link":505,"where_syntology_ran_a_sample":100,"not_listed_spam_title":0,"listed":2016,"listed_where_code_ran":100,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":77,"every_run_a_failure_of_syntologys_instrument":23,"listed_with_a_run_with_no_instrument_failure":77,"listed_every_run_a_failure_of_syntologys_instrument":23,"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/sensitivity","prev":"/task/sensitivity/papers/10","next":"/task/sensitivity/papers/12","papers":[{"url":null,"slug":"evaluation-of-gpt-3-for-anti-cancer-drug","title":"Evaluation of GPT-3 for Anti-Cancer Drug Sensitivity Prediction","date":"2023-09-18","arxiv_id":"2309.10016","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-credit-portfolio-sensitivity-to","title":"Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks","date":"2023-09-15","arxiv_id":"2309.08652","repositories_listed":0,"syntology":null},{"url":null,"slug":"linking-mechanisms-limits-and-robustness","title":"Quota Mechanisms: Finite-Sample Optimality and Robustness","date":"2023-09-14","arxiv_id":"2309.07363","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobile-object-tracking-in-panoramic-video-and","title":"Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection","date":"2023-09-12","arxiv_id":"2309.06592","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-analysis-for-linear-estimands","title":"Sensitivity Analysis for Linear Estimators","date":"2023-09-12","arxiv_id":"2309.06305","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-method-for-sensitivity","title":"Preserved Edge Convolutional Neural Network for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI)","date":"2023-09-08","arxiv_id":"2309.04100","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-uncertainty-quantification-in-a","title":"Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics","date":"2023-09-08","arxiv_id":"2309.04057","repositories_listed":0,"syntology":null},{"url":null,"slug":"precision-enhancement-of-distribution-system","title":"Precision Enhancement of Distribution System State Estimation via Tri-Objective Micro Phasor Measurement Unit Deployment","date":"2023-08-31","arxiv_id":"2309.00055","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-without-an-eigengap","title":"Gap-Free Clustering: Sensitivity and Robustness of SDP","date":"2023-08-29","arxiv_id":"2308.15642","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-relative-gaussian-mechanism-and-its","title":"The Relative Gaussian Mechanism and its Application to Private Gradient Descent","date":"2023-08-29","arxiv_id":"2308.15250","repositories_listed":0,"syntology":null},{"url":null,"slug":"epidenet-an-energy-efficient-approach-to","title":"EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems","date":"2023-08-28","arxiv_id":"2309.07135","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-active-learning-for-gaussian-process-based","title":"On Active Learning for Gaussian Process-based Global Sensitivity Analysis","date":"2023-08-27","arxiv_id":"2308.14220","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-machine-learning-modeling-for","title":"Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks","date":"2023-08-26","arxiv_id":"2308.13721","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressor-based-classification-for-atrial","title":"Compressor-Based Classification for Atrial Fibrillation Detection","date":"2023-08-25","arxiv_id":"2308.13328","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-intersection-of-video-capsule-endoscopy","title":"The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning","date":"2023-08-24","arxiv_id":"2308.13035","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-sensitivity-to-the","title":"Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions","date":"2023-08-22","arxiv_id":"2308.11483","repositories_listed":0,"syntology":null},{"url":null,"slug":"bundleseg-a-versatile-reliable-and","title":"BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation","date":"2023-08-21","arxiv_id":"2308.10958","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-sensor","title":"Reinforcement Learning Based Sensor Optimization for Bio-markers","date":"2023-08-21","arxiv_id":"2308.10649","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-branch-deep-learning-network-for","title":"Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy","date":"2023-08-19","arxiv_id":"2308.09945","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-analysis-of-ai-based-algorithms","title":"Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield","date":"2023-08-19","arxiv_id":"2308.11711","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-sensitivity-and-stability-of-deep","title":"Noise Sensitivity and Stability of Deep Neural Networks for Binary Classification","date":"2023-08-18","arxiv_id":"2308.09374","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathematical-modeling-of-the-treatment","title":"Mathematical modeling of the treatment response of resection plus combined chemotherapy and different types of radiation therapy in a glioblastoma patient","date":"2023-08-15","arxiv_id":"2308.07976","repositories_listed":0,"syntology":null},{"url":null,"slug":"faithful-to-whom-questioning-interpretability","title":"Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading","date":"2023-08-13","arxiv_id":"2308.06795","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-aware-mixed-precision","title":"Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation","date":"2023-08-12","arxiv_id":"2308.06422","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-distributional-impact-of-money-growth-and","title":"Money Growth and Inflation: A Quantile Sensitivity Approach","date":"2023-08-10","arxiv_id":"2308.05486","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-interference-mitigation-techniques-for","title":"Robust Interference Mitigation techniques for Direct Position Estimation","date":"2023-08-09","arxiv_id":"2308.05262","repositories_listed":0,"syntology":null},{"url":null,"slug":"safer-layer-level-sensitivity-assessment-for","title":"SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference","date":"2023-08-09","arxiv_id":"2308.04753","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-log-based-anomaly","title":"A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection","date":"2023-07-31","arxiv_id":"2307.16714","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-review-of-large-language-models","title":"A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI","date":"2023-07-28","arxiv_id":"2307.15425","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-invasive-diabetes-detection-using-gabor","title":"Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras","date":"2023-07-28","arxiv_id":"2307.15480","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-parameter-sensitivity","title":"Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia","date":"2023-07-27","arxiv_id":"2307.14654","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-estimator-for-the-sensitivity-to","title":"An Estimator for the Sensitivity to Perturbations of Deep Neural Networks","date":"2023-07-24","arxiv_id":"2307.12679","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-automated-cardiac-arrhythmia","title":"Development Of Automated Cardiac Arrhythmia Detection Methods Using Single Channel ECG Signal","date":"2023-07-23","arxiv_id":"2308.02405","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-analysis-of-test-data","title":"Information-theoretic Analysis of Test Data Sensitivity in Uncertainty","date":"2023-07-23","arxiv_id":"2307.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-learning-based-anomaly-detection-for","title":"Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis","date":"2023-07-20","arxiv_id":"2307.10596","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuron-sensitivity-guided-test-case-selection","title":"Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing","date":"2023-07-20","arxiv_id":"2307.11011","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-begining-of-the-trend-interest-rates","title":"The Beginning of the Trend: Interest Rates, Profits, and Markups","date":"2023-07-18","arxiv_id":"2307.08968","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-on-the-performance-of-generative-pre","title":"A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale","date":"2023-07-17","arxiv_id":"2307.08576","repositories_listed":0,"syntology":null},{"url":null,"slug":"available-observation-time-regulates-optimal","title":"Available observation time regulates optimal balance between sensitivity and confidence","date":"2023-07-15","arxiv_id":"2307.07794","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-sensitivity-of-deep-load","title":"On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks","date":"2023-07-14","arxiv_id":"2307.10209","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensi-bert-towards-sensitivity-driven-fine","title":"Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT","date":"2023-07-14","arxiv_id":"2307.11764","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-polynomial-chaos","title":"A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control","date":"2023-07-13","arxiv_id":"2307.07080","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-resolution-lung-nodule-segmentation-from","title":"Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks","date":"2023-07-13","arxiv_id":"2307.06547","repositories_listed":0,"syntology":null},{"url":null,"slug":"solvable-neural-network-model-for-input","title":"Solvable Neural Network Model for Input-Output Associations: Optimal Recall at the Onset of Chaos","date":"2023-07-11","arxiv_id":"2307.10197","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-induced-mask-transformers-for","title":"Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans","date":"2023-07-10","arxiv_id":"2307.04525","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-enabling-cardiac-digital-twins-of","title":"Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference","date":"2023-07-10","arxiv_id":"2307.04421","repositories_listed":0,"syntology":null},{"url":null,"slug":"climate-models-underestimate-the-sensitivity","title":"Climate Models Underestimate the Sensitivity of Arctic Sea Ice to Carbon Emissions","date":"2023-07-07","arxiv_id":"2307.03552","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-identification-and-quantification","title":"Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis","date":"2023-07-03","arxiv_id":"2307.01098","repositories_listed":0,"syntology":null},{"url":null,"slug":"pareto-optimal-proxy-metrics","title":"Pareto optimal proxy metrics","date":"2023-07-03","arxiv_id":"2307.01000","repositories_listed":0,"syntology":null},{"url":null,"slug":"population-age-group-sensitivity-for-covid-19","title":"Population Age Group Sensitivity for COVID-19 Infections with Deep Learning","date":"2023-07-03","arxiv_id":"2307.00751","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-of-heterodyne-rydberg","title":"Theoretical Analysis of Heterodyne Rydberg Atomic Receiver Sensitivity Based on Transit Relaxation Effect and Frequency Detuning","date":"2023-06-30","arxiv_id":"2306.17790","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cascaded-approach-for-ultraly-high","title":"A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans","date":"2023-06-28","arxiv_id":"2306.16036","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-automated-detection-of-microbleeds","title":"Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning","date":"2023-06-22","arxiv_id":"2306.13020","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-overall-sensitivity-of","title":"Evaluating the overall sensitivity of saliency-based explanation methods","date":"2023-06-21","arxiv_id":"2306.13682","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-validation-of-gibbs-algorithms","title":"On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation","date":"2023-06-21","arxiv_id":"2306.12380","repositories_listed":0,"syntology":null},{"url":null,"slug":"formal-covariate-benchmarking-to-bound","title":"Formal Covariate Benchmarking to Bound Omitted Variable Bias","date":"2023-06-18","arxiv_id":"2306.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-switching-policy-gradient-with","title":"Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling","date":"2023-06-15","arxiv_id":"2306.09554","repositories_listed":0,"syntology":null},{"url":null,"slug":"tighter-prediction-intervals-for-causal","title":"Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding","date":"2023-06-15","arxiv_id":"2306.09520","repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-verification-across-ages","title":"Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech","date":"2023-06-13","arxiv_id":"2306.07501","repositories_listed":0,"syntology":null},{"url":null,"slug":"general-transformation-for-consistent-online","title":"A Batch-to-Online Transformation under Random-Order Model","date":"2023-06-12","arxiv_id":"2306.07163","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-decoding-energy-reduction-using","title":"Video Decoding Energy Reduction Using Temporal-Domain Filtering","date":"2023-06-12","arxiv_id":"2306.06917","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-machine-learning-models-for-tau","title":"Trees versus Neural Networks for enhancing tau lepton real-time selection in proton-proton collisions","date":"2023-06-11","arxiv_id":"2306.06743","repositories_listed":0,"syntology":null},{"url":null,"slug":"merging-deep-learning-with-expert-knowledge","title":"Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy","date":"2023-06-08","arxiv_id":"2306.05572","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-alignment-breaking-the-trade-off","title":"Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception","date":"2023-06-05","arxiv_id":"2306.03229","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-aware-finetuning-for-accuracy","title":"Sensitivity-Aware Finetuning for Accuracy Recovery on Deep Learning Hardware","date":"2023-06-05","arxiv_id":"2306.03076","repositories_listed":0,"syntology":null},{"url":null,"slug":"regret-bounds-for-risk-sensitive-1","title":"Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures","date":"2023-06-04","arxiv_id":"2306.02399","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-and-robust-bayesian-selection-of","title":"Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization","date":"2023-06-01","arxiv_id":"2306.00357","repositories_listed":0,"syntology":null},{"url":null,"slug":"sharper-bounds-for-ell-p-sensitivity-sampling","title":"Sharper Bounds for $\\ell_p$ Sensitivity Sampling","date":"2023-06-01","arxiv_id":"2306.00732","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-analysis-of-high-dimensional","title":"Sensitivity Analysis of High-Dimensional Models with Correlated Inputs","date":"2023-05-31","arxiv_id":"2306.00555","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-analysis-of-rf-clust-for-leave","title":"Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction","date":"2023-05-30","arxiv_id":"2305.19375","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-of-slot-based-object-centric","title":"Sensitivity of Slot-Based Object-Centric Models to their Number of Slots","date":"2023-05-30","arxiv_id":"2305.18890","repositories_listed":0,"syntology":null},{"url":null,"slug":"policy-synthesis-and-reinforcement-learning","title":"Policy Synthesis and Reinforcement Learning for Discounted LTL","date":"2023-05-26","arxiv_id":"2305.17115","repositories_listed":0,"syntology":null},{"url":"/paper/action-sensitivity-learning-for-temporal","slug":"action-sensitivity-learning-for-temporal","title":"Action Sensitivity Learning for Temporal Action Localization","date":"2023-05-25","arxiv_id":"2305.15701","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximizing-soil-moisture-estimation-accuracy","title":"Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data","date":"2023-05-24","arxiv_id":"2305.15549","repositories_listed":0,"syntology":null},{"url":null,"slug":"pca-aided-calibration-of-systems-comprising","title":"PCA-aided calibration of systems comprising multiple unbiased sensors","date":"2023-05-24","arxiv_id":"2305.15568","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-brain-context-sensitivity-with-masked","title":"Probing Brain Context-Sensitivity with Masked-Attention Generation","date":"2023-05-23","arxiv_id":"2305.13863","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-non-linear-equations-for","title":"Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates","date":"2023-05-22","arxiv_id":"2305.13519","repositories_listed":0,"syntology":null},{"url":null,"slug":"at-admission-prediction-of-mortality-and","title":"At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study","date":"2023-05-18","arxiv_id":"2305.11199","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-and-robustness-of-large-language","title":"Sensitivity and Robustness of Large Language Models to Prompt Template in Japanese Text Classification Tasks","date":"2023-05-15","arxiv_id":"2305.08714","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-shaping-of-minimum-and-maximum","title":"Direct Shaping of Minimum and Maximum Singular Values: An $\\mathcal{H}_{-}/\\mathcal{H}_{\\infty}$ Synthesis Approach for Fault Detection Filters","date":"2023-05-12","arxiv_id":"2305.07258","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-sensitivity-of-automatic","title":"Investigating the Sensitivity of Automatic Speech Recognition Systems to Phonetic Variation in L2 Englishes","date":"2023-05-12","arxiv_id":"2305.07389","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-signal-propagation-in-resnets-through","title":"Field theory for optimal signal propagation in ResNets","date":"2023-05-12","arxiv_id":"2305.07715","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-wise-mixed-precision-quantization-of","title":"Patch-wise Mixed-Precision Quantization of Vision Transformer","date":"2023-05-11","arxiv_id":"2305.06559","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-user-level-private-convex-optimization","title":"On User-Level Private Convex Optimization","date":"2023-05-08","arxiv_id":"2305.04912","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-method-using-deep-learning-to-predict","title":"A new method using deep learning to predict the response to cardiac resynchronization therapy","date":"2023-05-04","arxiv_id":"2305.02475","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-different-retinal-regions-of","title":"Comparison of retinal regions-of-interest imaged by OCT for the classification of intermediate AMD","date":"2023-05-04","arxiv_id":"2305.02832","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-changes-when-you-randomly-choose-bpe","title":"What changes when you randomly choose BPE merge operations? Not much","date":"2023-05-04","arxiv_id":"2305.03029","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-tools-for-sensitivity-analysis-with","title":"Metric Tools for Sensitivity Analysis with Applications to Neural Networks","date":"2023-05-03","arxiv_id":"2305.02368","repositories_listed":0,"syntology":null},{"url":null,"slug":"jacobian-scaled-k-means-clustering-for","title":"Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows","date":"2023-05-02","arxiv_id":"2305.01539","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-paper-screening-for-clinical","title":"Automated Paper Screening for Clinical Reviews Using Large Language Models","date":"2023-05-01","arxiv_id":"2305.00844","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sensitivity-analysis-for-parametric","title":"Efficient Sensitivity Analysis for Parametric Robust Markov Chains","date":"2023-05-01","arxiv_id":"2305.01473","repositories_listed":0,"syntology":null},{"url":null,"slug":"attacks-on-robust-distributed-learning","title":"Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization","date":"2023-04-27","arxiv_id":"2304.14024","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-validation-of-model-less-robust","title":"Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients","date":"2023-04-26","arxiv_id":"2304.13638","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-models-shallow-again-jointly-learning","title":"Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference","date":"2023-04-26","arxiv_id":"2304.13274","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-implicit-shape-editing-using-boundary","title":"Neural Implicit Shape Editing using Boundary Sensitivity","date":"2023-04-24","arxiv_id":"2304.12951","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-sensitivity-estimation-without-a","title":"Spectral Sensitivity Estimation Without a Camera","date":"2023-04-23","arxiv_id":"2304.11549","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-analytical-estimation-of","title":"Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks","date":"2023-04-19","arxiv_id":"2304.09855","repositories_listed":0,"syntology":null},{"url":null,"slug":"split-merge-and-refine-fitting-tight-bounding","title":"Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search","date":"2023-04-10","arxiv_id":"2304.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-tolerant-control-design-in-scrubber","title":"Fault-Tolerant Control Design in Scrubber Plant with Fault on Sensor Sensitivity","date":"2023-04-09","arxiv_id":"2304.04765","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-performance-insensitivity-of-large","title":"Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search","date":"2023-04-08","arxiv_id":"2304.04071","repositories_listed":0,"syntology":null}],"record_sha256":"87e3ef20bee3c4059a08317d06ef1f2da8695466a3fa1a9ea9640479b81c2b3b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}