{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/ica/papers/2","list_of":"/method/ica","method":"ICA","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":2,"pages_in_order":3,"rows_per_page":100,"rows":[101,200],"of":261,"counts":{"archive_papers_tagged":261,"with_a_code_link":72,"where_syntology_ran_a_sample":15,"not_listed_spam_title":0,"listed":261,"listed_where_code_ran":15,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":13,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":13,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/ica","prev":"/method/ica","next":"/method/ica/papers/3","papers":[{"paper":"/paper/deep-deterministic-independent-component","slug":"deep-deterministic-independent-component","title":"Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing","date":"2022-02-07","arxiv_id":"2202.02951","n_code_links":1,"syntology":null},{"paper":null,"slug":"high-throughput-and-configurable-preprocessor","title":"Configurable Independent Component Analysis Preprocessing Accelerator","date":"2022-01-10","arxiv_id":"2201.03206","n_code_links":0,"syntology":null},{"paper":"/paper/traversing-within-the-gaussian-typical-set","slug":"traversing-within-the-gaussian-typical-set","title":"Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models","date":"2021-12-07","arxiv_id":"2112.03860","n_code_links":1,"syntology":{"ran":7,"of":15,"n_ran_checked":1,"n_instrument":6,"unverified":8,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","official":null}},{"paper":"/paper/binary-independent-component-analysis-via-non","slug":"binary-independent-component-analysis-via-non","title":"Binary Independent Component Analysis: A Non-stationarity-based Approach","date":"2021-11-30","arxiv_id":"2111.15431","n_code_links":1,"syntology":null},{"paper":null,"slug":"second-order-approximation-of-minimum","title":"Second-order Approximation of Minimum Discrimination Information in Independent Component Analysis","date":"2021-11-30","arxiv_id":"2111.15060","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-normative-and-biologically-plausible","title":"A Normative and Biologically Plausible Algorithm for Independent Component Analysis","date":"2021-11-17","arxiv_id":"2111.08858","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-latent-networks-in-resting-state","title":"Exploring latent networks in resting-state fMRI using voxel-to-voxel causal modeling feature selection","date":"2021-11-15","arxiv_id":"2111.07488","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-unmixing-of-raman-microscopic-images","title":"Spectral unmixing of Raman microscopic images of single human cells using Independent Component Analysis","date":"2021-10-25","arxiv_id":"2110.13189","n_code_links":0,"syntology":null},{"paper":null,"slug":"cortical-representations-of-auditory","title":"Cortical representations of Auditory Perception using Graph Independent Component on EEG","date":"2021-10-21","arxiv_id":"2110.12904","n_code_links":0,"syntology":null},{"paper":null,"slug":"similarity-and-independence-aware-beamformer-1","title":"Similarity-and-Independence-Aware Beamformer with Iterative Casting and Boost Start for Target Source Extraction Using Reference","date":"2021-10-18","arxiv_id":"2110.09019","n_code_links":0,"syntology":null},{"paper":"/paper/compressive-independent-component-analysis","slug":"compressive-independent-component-analysis","title":"Compressive Independent Component Analysis: Theory and Algorithms","date":"2021-10-15","arxiv_id":"2110.08045","n_code_links":1,"syntology":null},{"paper":"/paper/single-independent-component-recovery-and","slug":"single-independent-component-recovery-and","title":"Discovery of Single Independent Latent Variable","date":"2021-10-12","arxiv_id":"2110.05887","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["shaham-lab/disilv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"automatic-identification-of-the-end-diastolic","title":"Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos","date":"2021-10-06","arxiv_id":"2110.02844","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonlinear-ica-using-volume-preserving","title":"Nonlinear ICA Using Volume-Preserving Transformations","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/improving-phenotype-prediction-using-long","slug":"improving-phenotype-prediction-using-long","title":"Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity","date":"2021-09-07","arxiv_id":"2109.03115","n_code_links":1,"syntology":null},{"paper":"/paper/inference-via-sparse-coding-in-a-hierarchical","slug":"inference-via-sparse-coding-in-a-hierarchical","title":"Inference via Sparse Coding in a Hierarchical Vision Model","date":"2021-08-03","arxiv_id":"2108.01548","n_code_links":1,"syntology":null},{"paper":"/paper/assessment-of-deep-learning-based-heart-rate","slug":"assessment-of-deep-learning-based-heart-rate","title":"Assessment of Deep Learning-based Heart Rate Estimation using Remote Photoplethysmography under Different Illuminations","date":"2021-07-28","arxiv_id":"2107.13193","n_code_links":0,"syntology":null},{"paper":"/paper/discovering-latent-causal-variables-via","slug":"discovering-latent-causal-variables-via","title":"Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA","date":"2021-07-21","arxiv_id":"2107.10098","n_code_links":1,"syntology":null},{"paper":"/paper/functional-magnetic-resonance-imaging-data","slug":"functional-magnetic-resonance-imaging-data","title":"Functional Magnetic Resonance Imaging data augmentation through conditional ICA","date":"2021-07-11","arxiv_id":"2107.06104","n_code_links":2,"syntology":null},{"paper":null,"slug":"using-optimization-algorithms-for-control-of","title":"Using Optimization Algorithms for Control of Multiple Output DC-DC Converters","date":"2021-07-10","arxiv_id":"2107.04778","n_code_links":0,"syntology":null},{"paper":null,"slug":"atlas-based-segmentation-of-intracochlear","title":"Atlas-Based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks","date":"2021-07-08","arxiv_id":"2107.03987","n_code_links":0,"syntology":null},{"paper":"/paper/disentangling-identifiable-features-from","slug":"disentangling-identifiable-features-from","title":"Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA","date":"2021-06-17","arxiv_id":"2106.09620","n_code_links":1,"syntology":{"ran":23,"of":40,"n_ran_checked":5,"n_instrument":18,"unverified":17,"pointer_only":0,"phrase":"23 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 4 honoured, 1 violated, 0 with no contract checked; 18 where Syntology's instrument failed) · 17 unverified","official":{"repos":["HHalva/snica"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":17,"ran_from_kinds":["official"]}}},{"paper":"/paper/i-don-t-need-mathbf-u-identifiable-non-linear","slug":"i-don-t-need-mathbf-u-identifiable-non-linear","title":"I Don't Need u: Identifiable Non-Linear ICA Without Side Information","date":"2021-06-09","arxiv_id":"2106.05238","n_code_links":1,"syntology":null},{"paper":"/paper/deriving-autism-spectrum-disorder-functional","slug":"deriving-autism-spectrum-disorder-functional","title":"Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning","date":"2021-06-07","arxiv_id":"2106.09000","n_code_links":1,"syntology":null},{"paper":null,"slug":"higher-order-tensor-independent-component","title":"Higher-order tensor independent component analysis to realize MIMO remote sensing of respiration and heartbeat signals","date":"2021-05-03","arxiv_id":"2105.00723","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-the-effects-of-compression-on","title":"The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study","date":"2021-04-01","arxiv_id":"2104.00788","n_code_links":0,"syntology":null},{"paper":null,"slug":"fritl-a-hybrid-method-for-causal-discovery-in","title":"FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders","date":"2021-03-26","arxiv_id":"2103.14238","n_code_links":0,"syntology":null},{"paper":null,"slug":"full-encoder-make-autoencoders-learn-like-pca","title":"Learning Stable Representations with Full Encoder","date":"2021-03-25","arxiv_id":"2103.14082","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-multi-view-ica-estimation-of-noise","title":"Adaptive Multi-View ICA: Estimation of noise levels for optimal inference","date":"2021-02-22","arxiv_id":"2102.10964","n_code_links":0,"syntology":null},{"paper":"/paper/nonlinear-independent-component-analysis-for","slug":"nonlinear-independent-component-analysis-for","title":"Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time Signals","date":"2021-02-04","arxiv_id":"2102.02876","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-unified-view-for-unsupervised","title":"Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation","date":"2021-01-06","arxiv_id":"2101.02083","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-disentangled-representation","title":"Self-supervised Disentangled Representation Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-approximation-for-online-tensorial","title":"Stochastic Approximation for Online Tensorial Independent Component Analysis","date":"2020-12-28","arxiv_id":"2012.14415","n_code_links":0,"syntology":null},{"paper":null,"slug":"fat-tailed-factors","title":"Fat Tailed Factors","date":"2020-11-27","arxiv_id":"2011.13637","n_code_links":0,"syntology":null},{"paper":"/paper/an-open-framework-for-remote-ppg-methods-and","slug":"an-open-framework-for-remote-ppg-methods-and","title":"An Open Framework for Remote-PPG Methods and their Assessment","date":"2020-11-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"joint-analysis-of-structural-connectivity-and","title":"Joint analysis of structural connectivity and cortical surface features: correlates with mild traumatic brain injury","date":"2020-11-18","arxiv_id":"2012.03671","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-independent-component-analysis-with","title":"Spectral independent component analysis with noise modeling for M/EEG source separation","date":"2020-08-21","arxiv_id":"2008.09693","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonlinear-isa-with-auxiliary-variables-for","title":"Nonlinear ISA with Auxiliary Variables for Learning Speech Representations","date":"2020-07-25","arxiv_id":"2007.12948","n_code_links":0,"syntology":null},{"paper":"/paper/automated-intracranial-artery-labeling-using","slug":"automated-intracranial-artery-labeling-using","title":"Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement","date":"2020-07-11","arxiv_id":"2007.14472","n_code_links":1,"syntology":null},{"paper":null,"slug":"electromyogram-emg-removal-by-adding-sources","title":"Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2","date":"2020-07-07","arxiv_id":"2007.03136","n_code_links":0,"syntology":null},{"paper":null,"slug":"electromyogram-emg-removal-by-adding-sources-1","title":"Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 1","date":"2020-07-06","arxiv_id":"2007.03130","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-linear-identifiability-of-learned","title":"On Linear Identifiability of Learned Representations","date":"2020-07-01","arxiv_id":"2007.00810","n_code_links":0,"syntology":null},{"paper":"/paper/hidden-markov-nonlinear-ica-unsupervised","slug":"hidden-markov-nonlinear-ica-unsupervised","title":"Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series","date":"2020-06-22","arxiv_id":"2006.12107","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["HHalva/hmnlica"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"independent-component-analysis-for","title":"Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19","date":"2020-06-01","arxiv_id":"2006.01284","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-and-bayesian-deep-learning","title":"Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity","date":"2020-05-18","arxiv_id":"2005.08431","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-soft-and-flexible-fusion-of-eeg-and","title":"Early soft and flexible fusion of EEG and fMRI via tensor decompositions","date":"2020-05-12","arxiv_id":"2005.07134","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-l4-based-dictionary-learning","slug":"understanding-l4-based-dictionary-learning","title":"Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness","date":"2020-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-2-stage-imperialist-competitive","title":"Hybrid 2-stage Imperialist Competitive Algorithm with Ant Colony Optimization for Solving Multi-Depot Vehicle Routing Problem","date":"2020-04-07","arxiv_id":"2005.04157","n_code_links":0,"syntology":null},{"paper":null,"slug":"imperialist-competitive-algorithm-with","title":"Imperialist Competitive Algorithm with Independence and Constrained Assimilation for Solving 0-1 Multidimensional Knapsack Problem","date":"2020-03-14","arxiv_id":"2003.06617","n_code_links":0,"syntology":null},{"paper":null,"slug":"amateur-drones-detection-a-machine-learning","title":"Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference","date":"2020-02-28","arxiv_id":"2003.01519","n_code_links":0,"syntology":null},{"paper":"/paper/ice-beem-identifiable-conditional-energy","slug":"ice-beem-identifiable-conditional-energy","title":"ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA","date":"2020-02-26","arxiv_id":"2002.11537","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ilkhem/icebeem"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"learning-bijective-feature-maps-for-linear","title":"Learning Bijective Feature Maps for Linear ICA","date":"2020-02-18","arxiv_id":"2002.07766","n_code_links":0,"syntology":null},{"paper":"/paper/disentanglement-by-nonlinear-ica-with-general","slug":"disentanglement-by-nonlinear-ica-with-general","title":"Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)","date":"2020-01-14","arxiv_id":"2001.04872","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/wica-nonlinear-weighted-ica","slug":"wica-nonlinear-weighted-ica","title":"WICA: nonlinear weighted ICA","date":"2020-01-13","arxiv_id":"2001.04147","n_code_links":1,"syntology":null},{"paper":null,"slug":"robust-pose-invariant-shape-and-texture-based","title":"Robust Pose Invariant Shape and Texture based Hand Recognition","date":"2019-12-22","arxiv_id":"1912.10373","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-image-extracted-features-to-determine","title":"Using image-extracted features to determine heart rate and blink duration for driver sleepiness detection","date":"2019-11-04","arxiv_id":"1911.01333","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-contrastive-learning-and-nonlinear-ica","title":"Robust contrastive learning and nonlinear ICA in the presence of outliers","date":"2019-11-01","arxiv_id":"1911.00265","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-differentially-private-decentralized","title":"Improved Differentially Private Decentralized Source Separation for fMRI Data","date":"2019-10-28","arxiv_id":"1910.12913","n_code_links":0,"syntology":null},{"paper":"/paper/likelihood-free-overcomplete-ica-and","slug":"likelihood-free-overcomplete-ica-and","title":"Likelihood-Free Overcomplete ICA and Applications in Causal Discovery","date":"2019-09-04","arxiv_id":"1909.01525","n_code_links":1,"syntology":null},{"paper":null,"slug":"prosper-a-python-library-for-probabilistic","title":"ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions","date":"2019-08-01","arxiv_id":"1908.06843","n_code_links":0,"syntology":null},{"paper":"/paper/variational-autoencoders-and-nonlinear-ica-a","slug":"variational-autoencoders-and-nonlinear-ica-a","title":"Variational Autoencoders and Nonlinear ICA: A Unifying Framework","date":"2019-07-10","arxiv_id":"1907.04809","n_code_links":3,"syntology":{"ran":11,"of":17,"n_ran_checked":10,"n_instrument":1,"unverified":6,"pointer_only":1,"phrase":"11 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; 1 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":null,"slug":"empowering-swarm-based-optimizers-by-multi","title":"Modified swarm-based metaheuristics enhance Gradient Descent initialization performance: Application for EEG spatial filtering","date":"2019-06-13","arxiv_id":"1907.08220","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-analysis-of-cardiac-ct","title":"Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography","date":"2019-06-11","arxiv_id":"1906.04419","n_code_links":0,"syntology":null},{"paper":null,"slug":"190600028","title":"Independent Component Analysis based on multiple data-weighting","date":"2019-05-31","arxiv_id":"1906.00028","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-incomplete-rosetta-stone-problem","title":"The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA","date":"2019-05-16","arxiv_id":"1905.06642","n_code_links":0,"syntology":null},{"paper":null,"slug":"free-component-analysis-theory-algorithms","title":"Free Component Analysis: Theory, Algorithms & Applications","date":"2019-05-05","arxiv_id":"1905.01713","n_code_links":0,"syntology":null},{"paper":null,"slug":"isa-vae-independent-subspace-analysis-with","title":"ISA-VAE: Independent Subspace Analysis with Variational Autoencoders","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-gradient-based-ica-by-neurally","title":"Learning gradient-based ICA by neurally estimating mutual information","date":"2019-04-22","arxiv_id":"1904.09858","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-discovery-with-general-non-linear","title":"Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA","date":"2019-04-19","arxiv_id":"1904.09096","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-constrained-ica-emd-model-for-group-level","title":"A constrained ICA-EMD Model for Group Level fMRI Analysis","date":"2019-03-22","arxiv_id":"1903.09412","n_code_links":0,"syntology":null},{"paper":null,"slug":"non-linear-ica-based-on-cramer-wold-metric","title":"Non-linear ICA based on Cramer-Wold metric","date":"2019-03-01","arxiv_id":"1903.00201","n_code_links":0,"syntology":null},{"paper":"/paper/190504101","slug":"190504101","title":"Biologically plausible deep learning -- but how far can we go with shallow networks?","date":"2019-02-27","arxiv_id":"1905.04101","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-compositional-representations-of","title":"Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins","date":"2019-02-18","arxiv_id":"1902.06495","n_code_links":0,"syntology":null},{"paper":null,"slug":"visualizing-topographic-independent-component","title":"Visualizing Topographic Independent Component Analysis with Movies","date":"2019-01-24","arxiv_id":"1901.08239","n_code_links":0,"syntology":null},{"paper":"/paper/iclabel-an-automated-electroencephalographic","slug":"iclabel-an-automated-electroencephalographic","title":"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","date":"2019-01-22","arxiv_id":"1901.07915","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["lucapton/ICLabel-Train"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/dimensionality-reduction-and-bucket-ranking-a","slug":"dimensionality-reduction-and-bucket-ranking-a","title":"Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach","date":"2018-10-15","arxiv_id":"1810.06291","n_code_links":1,"syntology":null},{"paper":"/paper/specmar-fast-heart-rate-estimation-from-ppg","slug":"specmar-fast-heart-rate-estimation-from-ppg","title":"SPECMAR: Fast Heart Rate Estimation from PPG Signal using a Modified Spectral Subtraction Scheme with Composite Motion Artifacts Reference Generation","date":"2018-10-15","arxiv_id":"1810.06196","n_code_links":1,"syntology":null},{"paper":null,"slug":"linear-independent-component-analysis-over","title":"Linear Independent Component Analysis over Finite Fields: Algorithms and Bounds","date":"2018-09-16","arxiv_id":"1809.05815","n_code_links":0,"syntology":null},{"paper":null,"slug":"phd-dissertation-generalized-independent","title":"PhD Dissertation: Generalized Independent Components Analysis Over Finite Alphabets","date":"2018-09-13","arxiv_id":"1809.05043","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-achievability-of-blind-source","slug":"on-the-achievability-of-blind-source","title":"On the achievability of blind source separation for high-dimensional nonlinear source mixtures","date":"2018-08-02","arxiv_id":"1808.00668","n_code_links":1,"syntology":null},{"paper":null,"slug":"non-gaussian-component-analysis-using-entropy","title":"Non-Gaussian Component Analysis using Entropy Methods","date":"2018-07-13","arxiv_id":"1807.04936","n_code_links":0,"syntology":null},{"paper":"/paper/deep-density-destructors","slug":"deep-density-destructors","title":"Deep Density Destructors","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-likelihood-optimization-for-ica","title":"Accelerating likelihood optimization for ICA on real signals","date":"2018-06-25","arxiv_id":"1806.09390","n_code_links":0,"syntology":null},{"paper":"/paper/robustifying-independent-component-analysis","slug":"robustifying-independent-component-analysis","title":"Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise","date":"2018-06-04","arxiv_id":"1806.01094","n_code_links":3,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["sweichwald/coroICA-python"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/em-algorithms-for-ica","slug":"em-algorithms-for-ica","title":"Stochastic algorithms with descent guarantees for ICA","date":"2018-05-25","arxiv_id":"1805.10054","n_code_links":1,"syntology":null},{"paper":"/paper/nonlinear-ica-using-auxiliary-variables-and","slug":"nonlinear-ica-using-auxiliary-variables-and","title":"Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning","date":"2018-05-22","arxiv_id":"1805.08651","n_code_links":1,"syntology":null},{"paper":null,"slug":"independent-component-analysis-via-energy","title":"Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures","date":"2018-05-17","arxiv_id":"1805.06639","n_code_links":0,"syntology":null},{"paper":null,"slug":"randomized-ica-and-lda-dimensionality","title":"Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification","date":"2018-04-19","arxiv_id":"1804.07347","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-gaussian-ica","title":"Sparse Gaussian ICA","date":"2018-04-02","arxiv_id":"1804.00408","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-adaptive-sampling-for-kernel","title":"Distributed Adaptive Sampling for Kernel Matrix Approximation","date":"2018-03-27","arxiv_id":"1803.10172","n_code_links":0,"syntology":null},{"paper":"/paper/trace-your-sources-in-large-scale-data-one","slug":"trace-your-sources-in-large-scale-data-one","title":"Trace your sources in large-scale data: one ring to find them all","date":"2018-03-23","arxiv_id":"1803.08882","n_code_links":1,"syntology":null},{"paper":null,"slug":"detecting-non-causal-artifacts-in","title":"Detecting non-causal artifacts in multivariate linear regression models","date":"2018-03-02","arxiv_id":"1803.00810","n_code_links":0,"syntology":null},{"paper":null,"slug":"ica-based-on-split-generalized-gaussian","title":"ICA based on Split Generalized Gaussian","date":"2018-02-14","arxiv_id":"1802.05550","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-of-ica-and-iva-algorithms-with","title":"Development of ICA and IVA Algorithms with Application to Medical Image Analysis","date":"2018-01-25","arxiv_id":"1801.08600","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimization-and-testing-in-linear-non","title":"Optimization and Testing in Linear Non-Gaussian Component Analysis","date":"2017-12-23","arxiv_id":"1712.08837","n_code_links":0,"syntology":null},{"paper":"/paper/assessing-information-transmission-in-data","slug":"assessing-information-transmission-in-data","title":"Assessing Information Transmission in Data Transformations with the Channel Multivariate Entropy Triangle","date":"2017-11-30","arxiv_id":"1711.11510","n_code_links":1,"syntology":null},{"paper":"/paper/faster-ica-under-orthogonal-constraint","slug":"faster-ica-under-orthogonal-constraint","title":"Faster ICA under orthogonal constraint","date":"2017-11-29","arxiv_id":"1711.10873","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-analysis-of-the-myocardium-in","title":"Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis","date":"2017-11-24","arxiv_id":"1711.08917","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-scaling-limit-of-high-dimensional-online","title":"The Scaling Limit of High-Dimensional Online Independent Component Analysis","date":"2017-10-15","arxiv_id":"1710.05384","n_code_links":0,"syntology":null},{"paper":"/paper/learning-independent-features-with","slug":"learning-independent-features-with","title":"Learning Independent Features with Adversarial Nets for Non-linear ICA","date":"2017-10-13","arxiv_id":"1710.05050","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["pbrakel/anica"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"65b7396052700d6c12c4f93a073c1d0591699b7b939cb7b508ac870cb352cedf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}