Topological Data Analysis
125 papers with code • 0 benchmarks • 3 datasets
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Most implemented papers
Artificial Text Detection via Examining the Topology of Attention Maps
The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content.
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
tolgabirdal/phdimgeneralization
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NeurIPS 2021
Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters.
CUTS: A Framework for Multigranular Unsupervised Medical Image Segmentation
To address this, we present CUTS (Contrastive and Unsupervised Training for multi-granular medical image Segmentation), a fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that are not labeled or annotated.
Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics
Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues.
Can BERT eat RuCoLA? Topological Data Analysis to Explain
Our results contribute to understanding the behavior of monolingual LMs in the acceptability classification task, provide insights into the functional roles of attention heads, and highlight the advantages of TDA-based approaches for analyzing LMs.
Visualizing the Effects of a Changing Distance on Data Using Continuous Embeddings
The right scale is hard to pin down and it is preferable when results do not depend too tightly on the exact value one picked.
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams
This paper concerns itself with one popular topological feature, which is the number of $d-$dimensional holes in the dataset, also known as the Betti$-d$ number.
Inference of Ancestral Recombination Graphs through Topological Data Analysis
We build upon previous TDA developments for detecting and quantifying recombination, and present a novel framework that can be applied to hundreds of genomes and can be interpreted in terms of minimal histories of mutation and recombination events, quantifying the scales and identifying the genomic locations of recombinations.
Kernel method for persistence diagrams via kernel embedding and weight factor
Topological data analysis is an emerging mathematical concept for characterizing shapes in multi-scale data.
An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
Topological Data Analysis is a recent and fast growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data.
