On the public citation graph

Scalable approximate Bayesian inference for particle tracking data

https://doi.org/10.1101/276253
0 signed · 22 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/b978-0-08-051584-7.50027-9openalex-snapshot
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
inferredcites →https://doi.org/10.1038/nmeth.1237openalex-snapshot
Robust single-particle tracking in live-cell time-lapse sequences
inferredcites →https://doi.org/10.1038/nmeth.2808openalex-snapshot
Objective comparison of particle tracking methods
inferredcites →https://doi.org/10.1088/0034-4885/78/12/124601openalex-snapshot
A review of progress in single particle tracking: from methods to biophysical insights
inferredcites →https://doi.org/10.1098/rsos.160225openalex-snapshot
Automated single particle detection and tracking for large microscopy datasets
inferredcites →https://doi.org/10.1101/081703openalex-snapshot
Scalable variational inference for super resolution microscopy
inferredcites →https://doi.org/10.1101/153759openalex-snapshot
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
inferredcites →https://doi.org/10.1109/tmi.2008.916964openalex-snapshot
Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis
inferredcites →https://doi.org/10.1609/aaai.v31i1.10779openalex-snapshot
Structured Inference Networks for Nonlinear State Space Models
inferredcites →https://doi.org/10.21236/ada307097openalex-snapshot
Factorial Hidden Markov Models.
inferredcites →https://doi.org/10.3389/fncom.2017.00097openalex-snapshot
Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration
inferredcites →https://doi.org/10.48550/arxiv.1602.06701openalex-snapshot
Inference Networks for Sequential Monte Carlo in Graphical Models
inferredcites →https://doi.org/10.48550/arxiv.1605.08454openalex-snapshot
Linear dynamical neural population models through nonlinear embeddings
inferredcites →https://doi.org/10.48550/arxiv.1710.05741openalex-snapshot
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.media.2021.102128openalex-snapshot
Deep probabilistic tracking of particles in fluorescence microscopy images
inferred← citeshttps://doi.org/10.1021/cbmi.5c00146openalex-snapshot
Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights
inferred← citeshttps://doi.org/10.1101/2020.03.18.997924openalex-snapshot
YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina
inferred← citeshttps://doi.org/10.1109/tip.2020.2964515openalex-snapshot
A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections
inferred← citeshttps://doi.org/10.48550/arxiv.1811.09747openalex-snapshot
Amortized Bayesian inference for clustering models
inferred← citeshttps://doi.org/10.48550/arxiv.1901.00409openalex-snapshot
Neural Clustering Processes
inferred← citeshttps://doi.org/10.70675/bf8f07fdzcbc2z44f3z96bcz2a51c16db330openalex-snapshot
Tools to analyze single-particle tracking data in mammalian cells
inferred← citeshttps://doi.org/10.7554/elife.66410openalex-snapshot
Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.