{"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/dimensionality-reduction/papers/18","list_of":"/task/dimensionality-reduction","task":"Dimensionality Reduction","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":18,"pages_in_order":34,"rows_per_page":100,"rows":[1701,1800],"of":3304,"counts":{"archive_papers_tagged":3304,"with_a_code_link":857,"where_syntology_ran_a_sample":100,"not_listed_spam_title":0,"listed":3304,"listed_where_code_ran":100,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":84,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":84,"listed_every_run_a_failure_of_syntologys_instrument":16,"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/dimensionality-reduction","prev":"/task/dimensionality-reduction/papers/17","next":"/task/dimensionality-reduction/papers/19","papers":[{"url":null,"slug":"a-proper-orthogonal-decomposition-approach","title":"A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks","date":"2022-07-27","arxiv_id":"2207.13551","repositories_listed":0,"syntology":null},{"url":null,"slug":"laplacian-based-cluster-contractive-t-sne-for","title":"Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization","date":"2022-07-25","arxiv_id":"2207.12214","repositories_listed":0,"syntology":null},{"url":null,"slug":"textit-fastsvd-ml-rom-a-reduced-order","title":"$\\textit{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications","date":"2022-07-24","arxiv_id":"2207.11842","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssbnet-improving-visual-recognition","title":"SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling","date":"2022-07-23","arxiv_id":"2207.11511","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-supervised-tensor-dimension-reduction-based","title":"A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data","date":"2022-07-22","arxiv_id":"2207.11353","repositories_listed":0,"syntology":null},{"url":null,"slug":"principal-geodesic-analysis-of-merge-trees","title":"Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)","date":"2022-07-22","arxiv_id":"2207.10960","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sufficient-representation-learning-via","title":"Deep Sufficient Representation Learning via Mutual Information","date":"2022-07-21","arxiv_id":"2207.10772","repositories_listed":0,"syntology":null},{"url":null,"slug":"natural-language-processing-for","title":"Natural language processing for clusterization of genes according to their functions","date":"2022-07-17","arxiv_id":"2207.08162","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-mixed-precision-and-dimension","title":"Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation","date":"2022-07-16","arxiv_id":"2207.07931","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-approximation-for-general-tensor","title":"Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions","date":"2022-07-15","arxiv_id":"2207.07417","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-meta-learning-formulation-of-the","title":"A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction","date":"2022-07-14","arxiv_id":"2207.06676","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervising-embedding-algorithms-using-the","title":"On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods","date":"2022-07-14","arxiv_id":"2207.07218","repositories_listed":0,"syntology":null},{"url":null,"slug":"beam-space-mimo-radar-for-joint-communication","title":"Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation","date":"2022-07-12","arxiv_id":"2207.05337","repositories_listed":0,"syntology":null},{"url":null,"slug":"horizontal-and-vertical-attention-in","title":"Horizontal and Vertical Attention in Transformers","date":"2022-07-10","arxiv_id":"2207.04399","repositories_listed":0,"syntology":null},{"url":"/paper/udrn-unified-dimensional-reduction-neural","slug":"udrn-unified-dimensional-reduction-neural","title":"UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection","date":"2022-07-08","arxiv_id":"2207.03809","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-for-binary-classification","title":"A Hybrid Approach for Binary Classification of Imbalanced Data","date":"2022-07-06","arxiv_id":"2207.02738","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-sinusoidal-embeddings-enable","title":"Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data","date":"2022-07-06","arxiv_id":"2207.02980","repositories_listed":0,"syntology":null},{"url":null,"slug":"tricking-the-hashing-trick-a-tight-lower","title":"Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs","date":"2022-07-03","arxiv_id":"2207.00956","repositories_listed":0,"syntology":null},{"url":null,"slug":"uaem-itam-at-semeval-2022-task-5-vision","title":"UAEM-ITAM at SemEval-2022 Task 5: Vision-Language Approach to Recognize Misogynous Content in Memes","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-compression-of-sentence-transformer","title":"Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices","date":"2022-06-29","arxiv_id":"2207.12852","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-learning-for-dimensionality-reduction","title":"Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns","date":"2022-06-28","arxiv_id":"2206.13891","repositories_listed":0,"syntology":null},{"url":null,"slug":"cce-estimation-of-high-dimensional-panel-data","title":"Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects","date":"2022-06-24","arxiv_id":"2206.12152","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-assisted-1","title":"Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets","date":"2022-06-23","arxiv_id":"2206.11715","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-training-with-autoencoders","title":"Self-Supervised Training with Autoencoders for Visual Anomaly Detection","date":"2022-06-23","arxiv_id":"2206.11723","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-triplet-based-channel-charting-on","title":"Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements","date":"2022-06-20","arxiv_id":"2206.09774","repositories_listed":0,"syntology":null},{"url":null,"slug":"regression-of-high-dimensional-angular","title":"Regression of high dimensional angular momentum states of light","date":"2022-06-20","arxiv_id":"2206.09873","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-guided-rare-feature-selection-and-logic","title":"Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data","date":"2022-06-18","arxiv_id":"2206.09107","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpdr-a-novel-machine-learning-method-for-the","title":"DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction","date":"2022-06-17","arxiv_id":"2206.08974","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-gaussian-bernoulli-restricted","title":"Improved Gaussian-Bernoulli Restricted Boltzmann Machines for UAV-Ground Communication Systems","date":"2022-06-16","arxiv_id":"2206.08209","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-learning-of-parameterised-quantum","title":"Bayesian Learning of Parameterised Quantum Circuits","date":"2022-06-15","arxiv_id":"2206.07559","repositories_listed":0,"syntology":null},{"url":null,"slug":"broadband-beamforming-via-linear-embedding","title":"Broadband Beamforming via Linear Embedding","date":"2022-06-14","arxiv_id":"2206.07143","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-echo-state-network-evolving","title":"Evolutionary Echo State Network: evolving reservoirs in the Fourier space","date":"2022-06-10","arxiv_id":"2206.04951","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-mixtures-of-gaussians-for","title":"Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering","date":"2022-06-10","arxiv_id":"2206.04841","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-predictive-states-via-cantor","title":"Exploring Predictive States via Cantor Embeddings and Wasserstein Distance","date":"2022-06-09","arxiv_id":"2206.04198","repositories_listed":0,"syntology":null},{"url":null,"slug":"click-prediction-boosting-via-ensemble","title":"Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines","date":"2022-06-07","arxiv_id":"2206.03592","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-at-the-accuracy-limit-facing","title":"Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity","date":"2022-06-04","arxiv_id":"2206.01922","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-order-algorithms-for-min-max","title":"First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces","date":"2022-06-04","arxiv_id":"2206.02041","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-rule-interpretable-non-negative-data","title":"Finding Rule-Interpretable Non-Negative Data Representation","date":"2022-06-03","arxiv_id":"2206.01483","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-the-composition-of-feature","title":"Impact of the composition of feature extraction and class sampling in medicare fraud detection","date":"2022-06-03","arxiv_id":"2206.01413","repositories_listed":0,"syntology":null},{"url":null,"slug":"avida-alternating-method-for-visualizing-and","title":"AVIDA: Alternating method for Visualizing and Integrating Data","date":"2022-05-31","arxiv_id":"2206.00135","repositories_listed":0,"syntology":null},{"url":null,"slug":"principle-components-analysis-based","title":"Principal Component Analysis based frameworks for efficient missing data imputation algorithms","date":"2022-05-30","arxiv_id":"2205.15150","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-efficient-gaussian-tensor-network","title":"Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs","date":"2022-05-26","arxiv_id":"2205.13163","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-pretraining-and-transfer","title":"Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets","date":"2022-05-26","arxiv_id":"2205.13607","repositories_listed":0,"syntology":null},{"url":null,"slug":"pca-boosted-autoencoders-for-nonlinear","title":"PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes","date":"2022-05-23","arxiv_id":"2205.11673","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-forecasting-performance-of-the-factor","title":"The Forecasting performance of the Factor model with Martingale Difference errors","date":"2022-05-20","arxiv_id":"2205.10256","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-over-the-air-computation-for","title":"Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks","date":"2022-05-06","arxiv_id":"2205.03080","repositories_listed":0,"syntology":null},{"url":null,"slug":"lider-an-efficient-high-dimensional-learned","title":"LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval","date":"2022-05-02","arxiv_id":"2205.00970","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-classical-multiclass-linear","title":"Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution","date":"2022-05-02","arxiv_id":"2205.00668","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-dimensionality-reduction-method-based","title":"A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning","date":"2022-05-01","arxiv_id":"2205.02089","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-control-of-spatiotemporal-chaos","title":"Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning","date":"2022-05-01","arxiv_id":"2205.00579","repositories_listed":0,"syntology":null},{"url":null,"slug":"drone-flocking-optimization-using-nsga-ii-and","title":"Drone Flocking Optimization using NSGA-II and Principal Component Analysis","date":"2022-05-01","arxiv_id":"2205.00432","repositories_listed":0,"syntology":null},{"url":null,"slug":"representative-period-selection-for-power","title":"Representative period selection for power system planning using autoencoder-based dimensionality reduction","date":"2022-04-28","arxiv_id":"2204.13608","repositories_listed":0,"syntology":null},{"url":"/paper/bytecover2-towards-dimensionality-reduction","slug":"bytecover2-towards-dimensionality-reduction","title":"BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION","date":"2022-04-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-use-of-dimension-reduction-or-signal","title":"On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification","date":"2022-04-27","arxiv_id":"2204.13182","repositories_listed":0,"syntology":null},{"url":null,"slug":"trainable-compound-activation-functions-for","title":"Trainable Compound Activation Functions for Machine Learning","date":"2022-04-25","arxiv_id":"2204.12920","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimension-reduction-for-time-series-with","title":"Dimension Reduction for time series with Variational AutoEncoders","date":"2022-04-23","arxiv_id":"2204.11060","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressibility-power-of-pca-in-clustering","title":"Capturing the Denoising Effect of PCA via Compression Ratio","date":"2022-04-22","arxiv_id":"2204.10888","repositories_listed":0,"syntology":null},{"url":null,"slug":"delamination-prediction-in-composite-panels","title":"Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations","date":"2022-04-20","arxiv_id":"2204.09764","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamical-systems-based-framework-for","title":"A dynamical systems based framework for dimension reduction","date":"2022-04-18","arxiv_id":"2204.08155","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-dimensionality-reduction-techniques","title":"Exploring Dimensionality Reduction Techniques in Multilingual Transformers","date":"2022-04-18","arxiv_id":"2204.08415","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-recurrent-autoencoder-network","title":"Assessment of convolutional recurrent autoencoder network for learning wave propagation","date":"2022-04-12","arxiv_id":"2204.05573","repositories_listed":0,"syntology":null},{"url":null,"slug":"rmfgp-rotated-multi-fidelity-gaussian-process","title":"RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification","date":"2022-04-11","arxiv_id":"2204.04819","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-hop-tensor-representation-of-paths-in-graph","title":"T- Hop: Tensor representation of paths in graph convolutional networks","date":"2022-04-11","arxiv_id":"2204.04983","repositories_listed":0,"syntology":null},{"url":null,"slug":"covariance-matrix-preparation-for-quantum","title":"Covariance matrix preparation for quantum principal component analysis","date":"2022-04-07","arxiv_id":"2204.03495","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiauto-deeponet-a-multi-resolution","title":"MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems","date":"2022-04-07","arxiv_id":"2204.03193","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-real-time-target-tracking","title":"An efficient real-time target tracking algorithm using adaptive feature fusion","date":"2022-04-05","arxiv_id":"2204.02054","repositories_listed":0,"syntology":null},{"url":null,"slug":"nearly-minimax-robust-estimator-of-the-mean","title":"Nearly minimax robust estimator of the mean vector by iterative spectral dimension reduction","date":"2022-04-05","arxiv_id":"2204.02323","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-triplet-loss-and-nonlinear","title":"Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting","date":"2022-04-04","arxiv_id":"2204.13996","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlpro-a-system-for-hosting-crowdsourced","title":"MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems","date":"2022-04-04","arxiv_id":"2204.01216","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-dimensional-reduction-in","title":"Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents","date":"2022-04-01","arxiv_id":"2204.00642","repositories_listed":0,"syntology":null},{"url":null,"slug":"1-d-cnn-based-acoustic-scene-classification","title":"1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality","date":"2022-03-31","arxiv_id":"2204.00555","repositories_listed":0,"syntology":null},{"url":null,"slug":"ternary-and-binary-quantization-for-improved","title":"Ternary and Binary Quantization for Improved Classification","date":"2022-03-31","arxiv_id":"2203.16798","repositories_listed":0,"syntology":null},{"url":null,"slug":"digital-fingerprinting-of-microstructures","title":"Digital Fingerprinting of Microstructures","date":"2022-03-25","arxiv_id":"2203.13718","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-connection-between-locally-linear","title":"Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA","date":"2022-03-25","arxiv_id":"2203.13911","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-biosphere-computes-evolution-by","title":"Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction","date":"2022-03-22","arxiv_id":"2203.11891","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-modeling-for-fast-out-of","title":"Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection","date":"2022-03-20","arxiv_id":"2203.10422","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-and-wasserstein","title":"Dimensionality Reduction and Wasserstein Stability for Kernel Regression","date":"2022-03-17","arxiv_id":"2203.09347","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-plug-and-play-image","title":"Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients","date":"2022-03-14","arxiv_id":"2203.07308","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-and-prioritized","title":"Dimensionality Reduction and Prioritized Exploration for Policy Search","date":"2022-03-09","arxiv_id":"2203.04791","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-calibration-for-activity-based","title":"Bayesian Calibration for Activity Based Models","date":"2022-03-08","arxiv_id":"2203.04414","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-maps-using-the-semigroup-property","title":"Diffusion Maps : Using the Semigroup Property for Parameter Tuning","date":"2022-03-06","arxiv_id":"2203.02867","repositories_listed":0,"syntology":null},{"url":null,"slug":"uniform-approximations-for-randomized","title":"Uniform Approximations for Randomized Hadamard Transforms with Applications","date":"2022-03-03","arxiv_id":"2203.01599","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-split-semantic-detection-algorithm-for","title":"Split Semantic Detection in Sandplay Images","date":"2022-03-02","arxiv_id":"2203.00907","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-latent-factor-regression-and-sparse","title":"Are Latent Factor Regression and Sparse Regression Adequate?","date":"2022-03-02","arxiv_id":"2203.01219","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-robustness-of-countsketch-to-adaptive","title":"On the Robustness of CountSketch to Adaptive Inputs","date":"2022-02-28","arxiv_id":"2202.13736","repositories_listed":0,"syntology":null},{"url":null,"slug":"antler-bayesian-nonlinear-tensor-learning-and","title":"ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data","date":"2022-02-25","arxiv_id":"2202.13788","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalised-gaussian-process-latent-variable","title":"Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference","date":"2022-02-25","arxiv_id":"2202.12979","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-task-gaussian-process-over","title":"Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains","date":"2022-02-25","arxiv_id":"2202.12636","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-unfairness-of-dp-sgd-across","title":"Exploring the Unfairness of DP-SGD Across Settings","date":"2022-02-24","arxiv_id":"2202.12058","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-passenger-detection-with","title":"Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning","date":"2022-02-24","arxiv_id":"2202.11962","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dimensionality-reduction-method-for-finding","title":"A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence","date":"2022-02-23","arxiv_id":"2202.11598","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-motion-detection-using-sharpened","title":"Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering","date":"2022-02-23","arxiv_id":"2202.11667","repositories_listed":0,"syntology":null},{"url":"/paper/thermal-hand-image-segmentation-for-biometric","slug":"thermal-hand-image-segmentation-for-biometric","title":"Thermal hand image segmentation for biometric recognition","date":"2022-02-23","arxiv_id":"2202.11462","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-volatile-memory-accelerated-geometric","title":"Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis","date":"2022-02-21","arxiv_id":"2202.11518","repositories_listed":0,"syntology":null},{"url":null,"slug":"schrodinger-risk-diversification-portfolio","title":"Schrödinger Risk Diversification Portfolio","date":"2022-02-21","arxiv_id":"2202.09939","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-the-left-gram-matrix-to-cluster-high","title":"Using the left Gram matrix to cluster high dimensional data","date":"2022-02-16","arxiv_id":"2202.08236","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-differential-equations-for","title":"Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods","date":"2022-02-15","arxiv_id":"2202.07403","repositories_listed":0,"syntology":null},{"url":null,"slug":"taking-a-step-back-with-kcal-multi-class","title":"Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks","date":"2022-02-15","arxiv_id":"2202.07679","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generic-self-supervised-framework-of","title":"A Generic Self-Supervised Framework of Learning Invariant Discriminative Features","date":"2022-02-14","arxiv_id":"2202.06914","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-of-explainability-module-with-experts","title":"Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning","date":"2022-02-14","arxiv_id":"2202.06781","repositories_listed":0,"syntology":null}],"record_sha256":"7418d13dc14364790389053736ef50e67a63c6bc2d5422ad1f68600011be5343","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}