Fetching the paper…
Reading the bibliography…
Unsupervised graph representation learning has recently gained interest in several application domains such as neuroscience, where modeling the diverse morphology of cell types in the brain is one of the key challenges.
Histologie du système nerveux de l’homme et des vertébrés
Santiago Ramón y Cajal · 1911
Earlier work this paper cites.
Dendritic organization in the neurons of the visual and motor cortices of the cat
D. A. Sholl · 1953
Earlier work this paper cites.
Measures for quantifying dendritic arborizations
Harry BM Uylings and Jaap Van Pelt · 2002
Earlier work this paper cites.
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
Giorgio Ascoli, Lidia Alonso-Nanclares, Stewart Anderson, Germán Barrionuevo, Ruth Benavides-Piccione, Andreas Burkhalter, Gyorgy Buzsáki, Bruno Cauli, Javier Defelipe, and Alfonso Fairen · 2008
Earlier work this paper cites.
L-measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies
Ruggero Scorcioni, Sridevi Polavaram, and Giorgio A Ascoli · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Cell Type–Specific Three-Dimensional Structure of Thalamocortical Circuits in a Column of Rat Vibrissal Cortex
Marcel Oberlaender, Christiaan P. J. de Kock, Randy M. Bruno, Alejandro Ramirez, Hanno S. Meyer, Vincent J. Dercksen, Moritz Helmstaedter, and Bert Sakmann · 2012
Earlier work this paper cites.
New insights into the classification and nomenclature of cortical gabaergic interneurons
Javier Defelipe, Pedro López-Cruz, Ruth Benavides-Piccione, Concha Bielza, Pedro Larranaga, Stewart Anderson, Andreas Burkhalter, Bruno Cauli, Alfonso Fairen, Dirk Feldmeyer, Gord Fishell, David Fitzpatrick, Tamás Freund, Guillermo Gonzalez Burgos, Shaul Hestrin, Sean Hill, Patrick Hof, Josh Huang, Edward Jones, and Giorgio Ascoli · 2013
Earlier work this paper cites.
Statistical analysis and data mining of digital reconstructions of dendritic morphologies
Sridevi Polavaram, Todd A Gillette, Ruchi Parekh, and Giorgio A Ascoli · 2014
Earlier work this paper cites.
Towards the automatic classification of neurons
Rubén Armañanzas and Giorgio A. Ascoli · 2015
Earlier work this paper cites.
Electrophysiological, transcriptomic and morphologic profiling of single neurons using patch-seq
Cathryn Cadwell, Athanasia Palasantza, Xiaolong Jiang, Philipp Berens, Qiaolin Deng, Marlene Yilmaz, Jacob Reimer, Shan Shen, Matthias Bethge, Kimberley Tolias, Rickard Sandberg, and Andreas Tolias · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on l-measure
Yanbin Lu, Lawrence Carin, Ronald Coifman, William Shain, and Badrinath Roysam · 2015
Earlier work this paper cites.
Reconstruction and simulation of neocortical microcircuitry
Henry Markram, Eilif Muller, Srikanth Ramaswamy, Michael Reimann, Marwan Abdellah, Carlos Aguado, Anastasia Ailamaki, Lidia Alonso-Nanclares, Nicolas Antille, Selim Arsever, Atenekeng Kahou Guy Antoine, Thomas K Berger, Ahmet Bilgili, Nenad Buncic, Athanassia Chalimourda, Giuseppe Chindemi, Jean-Denis Courcol, Fabien Delalondre, Vincent Delattre, and Felix Schürmann · 2015
Earlier work this paper cites.
The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex
Srikanth Ramaswamy, Jean-Denis Courcol, Marwan Abdellah, Stanislaw R. Adaszewski, Nicolas Antille, Selim Arsever, Guy Atenekeng, Ahmet Bilgili, Yury Brukau, Athanassia Chalimourda, Giuseppe Chindemi, Fabien Delalondre, Raphael Dumusc, Stefan Eilemann, Michael Emiel Gevaert, Padraig Gleeson, Joe W. Graham, Juan B. Hernando, Lida Kanari, Yury Katkov, Daniel Keller, James G. King, Rajnish Ranjan, Michael W. Reimann, Christian Rössert, Ying Shi, Julian C. Shillcock, Martin Telefont, Werner Van Geit, Jafet Villafranca Diaz, Richard Walker, Yun Wang, Stefano M. Zaninetta, Javier DeFelipe, Sean L. Hill, Jeffrey Muller, Idan Segev, Felix Schürmann, Eilif B. Muller, and Henry Markram · 2015
Earlier work this paper cites.
Allen cell types database technical white paper: Cell morphology and histology
Allen Institute · 2016
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Earlier work this paper cites.
SGDR: stochastic gradient descent with restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
A topological representation of branching neuronal morphologies
Lida Kanari, Pawel Dlotko, Martina Scolamiero, Ran Levi, Julian C. Shillcock, Kathryn Hess, and Henry Markram · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 2017
Cited alongside, same era.
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
Later among the works it cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Later among the works it cites.
Graph-bert: Only attention is needed for learning graph representations
Jiawei Zhang, Haopeng Zhang, Congying Xia, and Li Sun · 2020
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Closest in time.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Closest in time.
Rethinking attention with performers
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, and Adrian Weller · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats
Yun Wang · 2018
Cited alongside, same era.
Classification of electrophysiological and morphological neuron types in the mouse visual cortex
Nathan Gouwens, Staci Sorensen, Jim Berg, Changkyu Lee, Tim Jarsky, Jonathan Ting, Susan Sunkin, David Feng, Costas Anastassiou, Eliza Barkan, Kris Bickley, Nicole Blesie, Thomas Braun, Krissy Brouner, Agata Budzillo, Shiella Caldejon, Tamara Casper, Dan Castelli, Peter Chong, and Christof Koch · 2019
Cited alongside, same era.
Objective morphological classification of neocortical pyramidal cells
Lida Kanari, Srikanth Ramaswamy, Ying Shi, Sebastien Morand, Julie Meystre, Rodrigo Perin, Marwan Abdellah, Yun Wang, Kathryn Hess, and Henry Markram · 2019
Cited alongside, same era.
Diffusion improves graph learning
Johannes Klicpera, Stefan Weiß enberger, and Stephan Günnemann · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Learning cellular morphology with neural networks
Philipp Schubert, Sven Dorkenwald, Michal Januszewski, Viren Jain, and Joergen Kornfeld · 2019
Cited alongside, same era.
Closest in time.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
Closest in time.
Consistent cross-modal identification of cortical neurons with coupled autoencoders
Rohan Gala, Agata Budzillo, Fahimeh Baftizadeh, Jeremy Miller, Nathan Gouwens, Anton Arkhipov, Gabe Murphy, Bosiljka Tasic, Hongkui Zeng, Michael Hawrylycz, et al · 2021
Closest in time.
Morphvae: Generating neural morphologies from 3d-walks using a variational autoencoder with spherical latent space
Sophie C. Laturnus and Philipp Berens · 2021
Closest in time.
Graphit: Encoding graph structure in transformers, 2021
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal · 2021
Closest in time.
Morphological diversity of single neurons in molecularly defined cell types
Hanchuan Peng, Peng Xie, Lijuan Liu, Xiuli Kuang, Yimin Wang, Lei Qu, Hui Gong, Shengdian Jiang, Anan Li, Zongcai Ruan, Liya Ding, Zizhen Yao, Chao Chen, Mengya Chen, Tanya Daigle, Rachel Dalley, Zhangcan Ding, Yanjun Duan, Aaron Feiner, and Hongkui Zeng · 2021
Closest in time.
E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Closest in time.
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Federico Scala, Dmitry Kobak, Matteo Bernabucci, Yves Bernaerts, Cathryn Cadwell, Jesus Castro, Leonard Hartmanis, Xiaolong Jiang, Sophie Laturnus, Elanine Miranda, Shalaka Mulherkar, Zheng Tan, Zizhen Yao, Hongkui Zeng, Rickard Sandberg, Philipp Berens, and Andreas Tolias · 2021
Closest in time.
Dataset: New insights into tree architecture from mobile laser scanning and geometry analysis
Dominik Seidel, Yonten Dorji, Bernhard Schuldt, Emilie Isasa, and Klaus Körber · 2021
Closest in time.
Self-supervised graph-level representation learning with local and global structure
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang · 2021
Closest in time.
Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Closest in time.
Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
Closest in time.
BEit: BERT pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
Closest in time.
Quantitative census of local somatic features in mouse visual cortex
Leila Elabbady, Sharmishtaa Seshamani, Shang Mu, Gayathri Mahalingam, Casey M Schneider-Mizell, Agnes Bodor, J Alexander Bae, Derrick Brittain, JoAnn Buchanan, Daniel J Bumbarger, et al · 2022
Closest in time.
Recipe for a general, powerful, scalable graph transformer, 2022
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
Closest in time.
Large-scale representation learning on graphs via bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Veličković, and Michal Valko · 2022
Closest in time.
Graph representation learning for large-scale neuronal morphological analysis
Jie Zhao, Xuejin Chen, Zhiwei Xiong, Zheng-Jun Zha, and Feng Wu · 2022
Closest in time.
Functional connectomics spanning multiple areas of mouse visual cortex
The MICrONS Consortium, J. Alexander Bae, Mahaly Baptiste, Caitlyn A. Bishop, Agnes L. Bodor, Derrick Brittain, JoAnn Buchanan, Daniel J. Bumbarger, Manuel A. Castro, Brendan Celii, Erick Cobos, Forrest Collman, Nuno Maçarico da Costa, Sven Dorkenwald, Leila Elabbady, Paul G. Fahey, Tim Fliss, Emmanouil Froudarakis, Jay Gager, Clare Gamlin, William Gray-Roncal, Akhilesh Halageri, James Hebditch, Zhen Jia, Emily Joyce, Justin Joyce, Chris Jordan, Daniel Kapner, Nico Kemnitz, Sam Kinn, Lindsey M. Kitchell, Selden Koolman, Kai Kuehner, Kisuk Lee, Kai Li, Ran Lu, Thomas Macrina, Gayathri Mahalingam, Jordan Matelsky, Sarah McReynolds, Elanine Miranda, Eric Mitchell, Shanka Subhra Mondal, Merlin Moore, Shang Mu, Taliah Muhammad, Barak Nehoran, Oluwaseun Ogedengbe, Christos Papadopoulos, Stelios Papadopoulos, Saumil Patel, Xaq Pitkow, Sergiy Popovych, Anthony Ramos, R. Clay Reid, Jacob Reimer, Patricia K. Rivlin, Victoria Rose, Casey M. Schneider-Mizell, H. Sebastian Seung, Ben Silverman, William Silversmith, Amy Sterling, Fabian H. Sinz, Cameron L. Smith, Shelby Suckow, Marc Takeno, Zheng H. Tan, Andreas S. Tolias, Russel Torres, Nicholas L. Turner, Edgar Y. Walker, Tianyu Wang, Adrian Wanner, Brock A. Wester, Grace Williams, Sarah Williams, Kyle Willie, Ryan Willie, William Wong, Jingpeng Wu, Chris Xu, Runzhe Yang, Dimitri Yatsenko, Fei Ye, Wenjing Yin, Rob Young, Szi chieh Yu, Daniel Xenes, and Chi Zhang · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
Closest in time.