Fetching the paper…
Reading the bibliography…
In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML).
A survey of recent advances in hierarchical clustering algorithms
F. Murtagh · 1983
Earlier work this paper cites.
A shortest augmenting path algorithm for dense and sparse linear assignment problems
R. Jonker · 1987
Earlier work this paper cites.
Tree visualization with tree-maps: 2-d space-filling approach
B. Shneiderman · 1992
Earlier work this paper cites.
The eyes have it: A task by data type taxonomy for information visualizations
B. Shneiderman · 1996
Earlier work this paper cites.
PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps
B. B. Bederson · 2001
Earlier work this paper cites.
Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies
B. B. Bederson, B. Shneiderman, and M. Wattenberg · 2002
Earlier work this paper cites.
Interactively exploring hierarchical clustering results [gene identification]
J. Seo and B. Shneiderman · 2002
Earlier work this paper cites.
Low-level components of analytic activity in information visualization
R. Amar, J. Eagan, and J. Stasko · 2005
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. E. Hinton and R. R. Salakhutdinov · 2006
Earlier work this paper cites.
Toward a deeper understanding of the role of interaction in information visualization
J. S. Yi, Y. ah Kang, J. Stasko, and J. A. Jacko · 2007
Earlier work this paper cites.
CAT: A hierarchical image browser using a rectangle packing technique
A. Gomi, R. Miyazaki, T. Itoh, and J. Li · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Modern hierarchical, agglomerative clustering algorithms
D. Müllner · 2011
Earlier work this paper cites.
Improving visualization of large hierarchical clustering
G. Bisson and R. Blanch · 2012
Earlier work this paper cites.
Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization
J. Choo, C. Lee, C. K. Reddy, and H. Park · 2013
Earlier work this paper cites.
HierarchicalTopics: Visually exploring large text collections using topic hierarchies
W. Dou, L. Yu, X. Wang, Z. Ma, and W. Ribarsky · 2013
Earlier work this paper cites.
Nmap: A novel neighborhood preservation space-filling algorithm
F. S. Duarte, F. Sikansi, F. M. Fatore, S. G. Fadel, and F. V. Paulovich · 2014
Earlier work this paper cites.
t-sne visualization of cnn codes
A. Karpathy · 2014
Earlier work this paper cites.
Accelerating t-sne using tree-based algorithms
L. van der Maaten · 2014
Earlier work this paper cites.
Towards interactive, intelligent, and integrated multimedia analytics
J. Zahálka and M. Worring · 2014
Earlier work this paper cites.
ModelTracker: Redesigning performance analysis tools for machine learning
S. Amershi, M. Chickering, S. M. Drucker, B. Lee, P. Simard, and J. Suh · 2015
Earlier work this paper cites.
Isomatch: Creating informative grid layouts
O. Fried, S. DiVerdi, M. Halber, E. Sizikova, and A. Finkelstein · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Topiclens: Efficient multi-level visual topic exploration of large-scale document collections
M. Kim, K. Kang, D. Park, J. Choo, and N. Elmqvist · 2016
Cited alongside, same era.
Hierarchical stochastic neighbor embedding
N. Pezzotti, T. Höllt, B. Lelieveldt, E. Eisemann, and A. Vilanova · 2016
Cited alongside, same era.
Visualizing the hidden activity of artificial neural networks
P. E. Rauber, S. G. Fadel, A. X. Falcao, and A. C. Telea · 2016
Cited alongside, same era.
Squares: Supporting interactive performance analysis for multiclass classifiers
D. Ren, S. Amershi, B. Lee, J. Suh, and J. D. Williams · 2016
Cited alongside, same era.
Embedding projector: Interactive visualization and interpretation of embeddings
D. Smilkov, N. Thorat, C. Nicholson, E. Reif, F. B. Viégas, and M. Wattenberg · 2016
Cited alongside, same era.
Errudite: Scalable, reproducible, and testable error analysis
T. Wu, M. T. Ribeiro, J. Heer, and D. Weld · 2019
Later among the works it cites.
Interactive correction of mislabeled training data
S. Xiang, X. Ye, J. Xia, J. Wu, Y. Chen, and S. Liu · 2019
Later among the works it cites.
t-viSNE: Interactive assessment and interpretation of t-SNE projections
A. Chatzimparmpas, R. M. Martins, and A. Kerren · 2020
Later among the works it cites.
OoDAnalyzer: Interactive analysis of out-of-distribution samples
C. Chen, J. Yuan, Y. Lu, Y. Liu, H. Su, S. Yuan, and S. Liu · 2020
Later among the works it cites.
DECE: Decision explorer with counterfactual explanations for machine learning models
F. Cheng, Y. Ming, and H. Qu · 2020
Later among the works it cites.
ViCE: Visual counterfactual explanations for machine learning models
O. Gomez, S. Holter, J. Yuan, and E. Bertini · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
ICLIC: Interactive categorization of large image collections
P. Van Der Corput and J. J. van Wijk · 2016
Cited alongside, same era.
How to use t-SNE effectively
M. Wattenberg, F. Viégas, and I. Johnson · 2016
Cited alongside, same era.
ActiVis: Visual exploration of industry-scale deep neural network models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau · 2017
Cited alongside, same era.
Atom: A grammar for unit visualizations
D. Park, S. M. Drucker, R. Fernandez, and N. Elmqvist · 2017
Cited alongside, same era.
Facets: An open source visualization tool for machine learning training data, 2017
J. Wexler · 2017
Cited alongside, same era.
Gender Shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
Cited alongside, same era.
Visual analytics in deep learning: An interrogative survey for the next frontiers
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau · 2018
Cited alongside, same era.
Later among the works it cites.
Crowdsourcing the perception of machine teaching
J. Hong, K. Lee, J. Xu, and H. Kacorri · 2020
Later among the works it cites.
Interactive steering of hierarchical clustering
W. Yang, X. Wang, J. Lu, W. Dou, and S. Liu · 2020
Later among the works it cites.
A survey of visual analytics techniques for machine learning
J. Yuan, C. Chen, W. Yang, M. Liu, J. Xia, and S. Liu · 2020
Later among the works it cites.
II-20: Intelligent and pragmatic analytic categorization of image collections
J. Zahálka, M. Worring, and J. J. Van Wijk · 2020
Later among the works it cites.
Where can we help? a visual analytics approach to diagnosing and improving semantic segmentation of movable objects
W. He, L. Zou, A. K. Shekar, L. Gou, and L. Ren · 2021
Later among the works it cites.
ExplorerTree: a focus+context exploration approach for 2D embeddings
W. E. Marcílio-Jr, D. M. Eler, F. V. Paulovich, J. F. Rodrigues-Jr, and A. O. Artero · 2021
Later among the works it cites.
A chat with andrew on mlops: From model-centric to data-centric ai, 2021
A. Ng · 2021
Later among the works it cites.
Pervasive label errors in test sets destabilize machine learning benchmarks
C. G. Northcutt, A. Athalye, and J. Mueller · 2021
Later among the works it cites.
Contrastive identification of covariate shift in image data
M. L. Olson, T.-V. Nguyen, G. Dixit, N. Ratzlaff, W.-K. Wong, and M. Kahng · 2021
Later among the works it cites.
What can data-centric ai learn from data and ml engineering?
N. Polyzotis and M. Zaharia · 2021
Later among the works it cites.
Know Your Data: a new tool to explore datasets, 2021
D. Smilkov, N. Thorat, M. Pellat, and L. Peran · 2021
Later among the works it cites.
Understanding how dimension reduction tools work: an empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization
Y. Wang, H. Huang, C. Rudin, and Y. Shaposhnik · 2021
Later among the works it cites.
Data collection and quality challenges in deep learning: A data-centric ai perspective
S. E. Whang, Y. Roh, H. Song, and J.-G. Lee · 2021
Later among the works it cites.
Human-in-the-loop extraction of interpretable concepts in deep learning models
Z. Zhao, P. Xu, C. Scheidegger, and L. Ren · 2021
Later among the works it cites.
Image t-SNE live
G. Kogan · 2022
Closest in time.
tsne-grid
R. Ratajczak · 2022
Closest in time.
Basic classification: Classify images of clothing
TensorFlow · 2022
Closest in time.