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Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements.
Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi · 1987
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Spoken letter recognition
Mark Fanty and Ronald Cole · 1991
Earlier work this paper cites.
Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
Earlier work this paper cites.
Newsweeder: Learning to filter netnews
Ken Lang · 1995
Earlier work this paper cites.
Columbia object image library (coil-20)
Sameer A Nene, Shree K Nayar, Hiroshi Murase, et al · 1996
Earlier work this paper cites.
Wrappers for feature subset selection
Ron Kohavi and George H John · 1997
Earlier work this paper cites.
Assessing artificial neural network pruning algorithms
Taskin Kavzoglu and Paul M Mather · 1998
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Feature extraction, construction and selection: A data mining perspective , volume 453
Huan Liu and Hiroshi Motoda · 1998
Earlier work this paper cites.
Scipy: Open source scientific tools for python
Eric Jones, Travis Oliphant, and Pearu Peterson · 2001
Earlier work this paper cites.
Classification and regression by randomforest
Andy Liaw, Matthew Wiener, et al · 2002
Earlier work this paper cites.
The architecture of complex weighted networks
Alain Barrat, Marc Barthelemy, Romualdo Pastor-Satorras, and Alessandro Vespignani · 2004
Earlier work this paper cites.
Feature selection for unsupervised learning
Jennifer G Dy and Carla E Brodley · 2004
Earlier work this paper cites.
Extremely randomized trees
Pierre Geurts, Damien Ernst, and Louis Wehenkel · 2006
Earlier work this paper cites.
Laplacian score for feature selection
Xiaofei He, Deng Cai, and Partha Niyogi · 2006
Earlier work this paper cites.
Embedded methods
Thomas Navin Lal, Olivier Chapelle, Jason Weston, and André Elisseeff · 2006
Earlier work this paper cites.
Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain
Lixin Sun, Ai-Min Hui, Qin Su, Alexander Vortmeyer, Yuri Kotliarov, Sandra Pastorino, Antonino Passaniti, Jayant Menon, Jennifer Walling, Rolando Bailey, et al · 2006
Earlier work this paper cites.
Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer
Avrum Spira, Jennifer E Beane, Vishal Shah, Katrina Steiling, Gang Liu, Frank Schembri, Sean Gilman, Yves-Martine Dumas, Paul Calner, Paola Sebastiani, et al · 2007
Earlier work this paper cites.
Semi-supervised feature selection via spectral analysis
Zheng Zhao and Huan Liu · 2007
Earlier work this paper cites.
Feature extraction: foundations and applications , volume 207
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti A Zadeh · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Dimensionality reduction: a comparative
Laurens Van Der Maaten, Eric Postma, and Jaap Van den Herik · 2009
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Unsupervised feature selection for multi-cluster data
Deng Cai, Chiyuan Zhang, and Xiaofei He · 2010
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L2, 1-norm regularized discriminative feature selection for unsupervised
Yi Yang, Heng Tao Shen, Zhigang Ma, Zi Huang, and Xiaofang Zhou · 2011
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Autoencoders, unsupervised learning, and deep architectures
Pierre Baldi · 2012
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A survey on semi-supervised feature selection methods
Razieh Sheikhpour, Mehdi Agha Sarram, Sajjad Gharaghani, and Mohammad Ali Zare Chahooki · 2017
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Mission: Ultra large-scale feature selection using count-sketches
Amirali Aghazadeh, Ryan Spring, Daniel Lejeune, Gautam Dasarathy, Anshumali Shrivastava, et al · 2018
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Autoencoder inspired unsupervised feature selection
Kai Han, Yunhe Wang, Chao Zhang, Chao Li, and Chao Xu · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Efficient greedy feature selection for unsupervised learning
Ahmed K Farahat, Ali Ghodsi, and Mohamed S Kamel · 2013
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A survey on feature selection methods
Girish Chandrashekar and Ferat Sahin · 2014
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Towards ultrahigh dimensional feature selection for big data
Mingkui Tan, Ivor W Tsang, and Li Wang · 2014
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Feature selection: A data perspective
Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu · 2018
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Deeppink: reproducible feature selection in deep neural networks
Yang Lu, Yingying Fan, Jinchi Lv, and William Stafford Noble · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
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Ai-powered green cloud and data center
Jun Yang, Wenjing Xiao, Chun Jiang, M Shamim Hossain, Ghulam Muhammad, and Syed Umar Amin · 2018
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Concrete autoencoders: Differentiable feature selection and reconstruction
Muhammed Fatih Balın, Abubakar Abid, and James Zou · 2019
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Cognitive model priors for predicting human decisions
David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, and Thomas L. Griffiths · 2019
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Sparse networks from scratch: Faster training without losing performance
Tim Dettmers and Luke Zettlemoyer · 2019
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Agnostic feature selection
Guillaume Doquet and Michèle Sebag · 2019
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Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2019
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Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa and Xin Wang · 2019
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Multi-objective evolutionary federated learning
Hangyu Zhu and Yaochu Jin · 2019
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Assessment list for trustworthy artificial intelligence (ALTAI) for self-assessment, 2020
AI High-Level Expert Group · 2020
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Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferrar, Ghada Sokar, Mykola Pechenizkiy, and Decebal C Mocanu · 2020
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Fsnet: Feature selection network on high-dimensional biological data
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