Fetching the paper…
Reading the bibliography…
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
Mingxing Tan and Quoc Le. 2019 · 1905
Earlier work this paper cites.
The visual display of quantitative information
Edward R Tufte. 1986 · 1986
Earlier work this paper cites.
Organizational culture . Vol. 45
Edgar H Schein. 1990 · 1990
Earlier work this paper cites.
The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings 1996 IEEE Symposium on Visual Languages . IEEE, 336–343
Ben Shneiderman. 1996 · 1996
Earlier work this paper cites.
Principles of mixed-initiative user interfaces. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems . 159–166
Eric Horvitz. 1999 · 1999
Earlier work this paper cites.
A general inductive approach for qualitative data analysis
David R Thomas. 2003 · 2003
Earlier work this paper cites.
Conducting in-depth interviews: A guide for designing and conducting in-depth interviews for evaluation input . Vol. 2
Carolyn Boyce and Palena Neale. 2006 · 2006
Earlier work this paper cites.
Thematic coding and categorizing
Graham R Gibbs. 2007 · 2007
Earlier work this paper cites.
Investigating statistical machine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . 667–676
Kayur Patel, James Fogarty, James A Landay, and Beverly Harrison. 2008 · 2008
Earlier work this paper cites.
A review of overview+detail, zooming, and focus+context interfaces
Andy Cockburn, Amy Karlson, and Benjamin B Bederson. 2009 · 2009
Earlier work this paper cites.
iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction. In 2010 IEEE Symposium on Visual Analytics Science and Technology . IEEE, 27–34
Jaegul Choo, Hanseung Lee, Jaeyeon Kihm, and Haesun Park. 2010 · 2010
Earlier work this paper cites.
A multi-level typology of abstract visualization tasks
Matthew Brehmer and Tamara Munzner. 2013 · 2013
Earlier work this paper cites.
Machine learning: The high interest credit card of technical debt
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young. 2014 · 2014
Earlier work this paper cites.
Modeltracker: Redesigning performance analysis tools for machine learning. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems . 337–346
Saleema Amershi, Max Chickering, Steven M Drucker, Bongshin Lee, Patrice Simard, and Jina Suh. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J Dally. 2016 · 2016
Earlier work this paper cites.
Visual exploration of machine learning results using data cube analysis. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics . 1–6
Minsuk Kahng, Dezhi Fang, and Duen Horng Chau. 2016 · 2016
Earlier work this paper cites.
Jupyter Notebooks-a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B Hamrick, Jason Grout, Sylvain Corlay, et al · 2016
Earlier work this paper cites.
Squares: Supporting interactive performance analysis for multiclass classifiers
Donghao Ren, Saleema Amershi, Bongshin Lee, Jina Suh, and Jason D Williams. 2016 · 2016
Earlier work this paper cites.
Model compression and acceleration for deep neural networks: The principles, progress, and challenges
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2018 · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017 · 2017
Earlier work this paper cites.
Using the Jupyter notebook as a tool for open science: An empirical study. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL) . IEEE, 1–2
Bernadette M Randles, Irene V Pasquetto, Milena S Golshan, and Christine L Borgman. 2017 · 2017
Earlier work this paper cites.
Netron, visualizer for neural network, deep learning, and machine learning models
Lutz Roeder. 2017 · 2017
Earlier work this paper cites.
LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M Rush. 2017 · 2017
Earlier work this paper cites.
Visualization and pruning of SSD with the base network VGG16. In Proceedings of the 2017 International Conference on Deep Learning Technologies . 90–94
Xuemei Xie, Xiao Han, Quan Liao, and Guangming Shi. 2017 · 2017
Earlier work this paper cites.
Visualizing compression of deep learning models for classification. In 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) . IEEE, 1–8
Marissa Dotter and Chris M Ward. 2018 · 2018
Earlier work this paper cites.
Visual analytics in deep learning: An interrogative survey for the next frontiers
Fred Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. 2018 · 2018
Earlier work this paper cites.
Learning IoT in edge: Deep learning for the nternet of Things with edge computing
He Li, Kaoru Ota, and Mianxiong Dong. 2018 · 2018
Earlier work this paper cites.
Model compression via distillation and quantization
Antonio Polino, Razvan Pascanu, and Dan Alistarh. 2018 · 2018
Earlier work this paper cites.
Quantization
PyTorch. 2018 · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and pattern Recognition . 4510–4520
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018 · 2018
Earlier work this paper cites.
Introducing the Model Optimization Toolkit for TensorFlow
TensorFlow. 2018 · 2018
Earlier work this paper cites.
Visualizing dataflow graphs of deep learning models in TensorFlow
Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, and Martin Wattenberg. 2018 · 2018
Earlier work this paper cites.
Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions. In International Conference on Machine Learning . PMLR, 5363–5372
Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, and Yingyan Lin. 2018 · 2018
Earlier work this paper cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 6848–6856
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2018 · 2018
Cited alongside, same era.
Fairsight: Visual analytics for fairness in decision making
Yongsu Ahn and Yu-Ru Lin. 2019 · 2019
Cited alongside, same era.
Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice . IEEE, 291–300
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019 · 2019
Cited alongside, same era.
Google colaboratory
Ekaba Bisong and Ekaba Bisong. 2019 · 2019
Cited alongside, same era.
FairVis: Visual analytics for discovering intersectional bias in machine learning. In IEEE Conference on Visual Analytics Science and Technology . IEEE, 46–56
Know Your Data
Google Inc. 2021 · 2021
Later among the works it cites.
Neural network intelligence
Microsoft. 2021 · 2021
Later among the works it cites.
Machine learning at the network edge: A survey
MG Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, and Faraz Hussain. 2021 · 2021
Later among the works it cites.
OpenAI Codex
OpenAI. 2021 · 2021
Later among the works it cites.
Mingjian Zhu, Kai Han, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, and Yunhe Wang. 2021 · 2021
Later among the works it cites.
Deploying transformers on the Apple Neural Engine
Apple. 2022a · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, and Duen Horng Chau. 2019 · 2019
Cited alongside, same era.
Managing messes in computational notebooks. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . 1–12
Andrew Head, Fred Hohman, Titus Barik, Steven M Drucker, and Robert DeLine. 2019 · 2019
Cited alongside, same era.
exbert: A visual analysis tool to explore learned representations in transformers models
Benjamin Hoover, Hendrik Strobelt, and Sebastian Gehrmann. 2019 · 2019
Cited alongside, same era.
Towards effective foraging by data scientists to find past analysis choices. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . 1–13
Mary Beth Kery, Bonnie E John, Patrick O’Flaherty, Amber Horvath, and Brad A Myers. 2019 · 2019
Cited alongside, same era.
Sparisty
PyTorch. 2019 · 2019
Cited alongside, same era.
Guidelines and benchmarks for deployment of deep learning models on smartphones as real-time apps
Abhishek Sehgal and Nasser Kehtarnavaz. 2019 · 2019
Cited alongside, same era.
Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers
Pete Warden and Daniel Situnayake. 2019 · 2019
Cited alongside, same era.
The what-if tool: Interactive probing of machine learning models
James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda Viégas, and Jimbo Wilson. 2019 · 2019
Cited alongside, same era.
A multi-task neural architecture for on-device scene analysis
Apple. 2022b · 2022
Later among the works it cites.
Symphony: Composing interactive interfaces for machine learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, and Dominik Moritz. 2022 · 2022
Later among the works it cites.
DendroMap: Visual exploration of large-scale image datasets for machine learning with treemaps
Donald Bertucci, Md Montaser Hamid, Yashwanthi Anand, Anita Ruangrotsakun, Delyar Tabatabai, Melissa Perez, and Minsuk Kahng. 2022 · 2022
Later among the works it cites.
Minsik Cho, Keivan A. Vahid, Saurabh Adya, and Mohammad Rastegari. 2022 · 2022
Later among the works it cites.
Artificial intelligence
Charlie Giattino, Edouard Mathieu, Veronika Samborska, Julia Broden, and Max Roser. 2022 · 2022
Later among the works it cites.
Why on-device machine learning?
Google. Accessed 2022 · 2022
Later among the works it cites.
Neo: Generalizing confusion matrix visualization to hierarchical and multi-output labels. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM
Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, and Kayur Patel. 2022 · 2022
Later among the works it cites.
Interviews in the social sciences
Eleanor Knott, Aliya Hamid Rao, Kate Summers, and Chana Teeger. 2022 · 2022
Later among the works it cites.
Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
Hendrik Strobelt, Albert Webson, Victor Sanh, Benjamin Hoover, Johanna Beyer, Hanspeter Pfister, and Alexander M Rush. 2022 · 2022
Later among the works it cites.
An improved one millisecond mobile backbone
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, and Anurag Ranjan. 2022 · 2022
Later among the works it cites.
Machine learning model sizes and the parameter gap
Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Anson Ho, and Marius Hobbhahn. 2022 · 2022
Later among the works it cites.
VAC-CNN: A visual analytics system for comparative studies of deep convolutional neural networks
Xiwei Xuan, Xiaoyu Zhang, Oh-Hyun Kwon, and Kwan-Liu Ma. 2022 · 2022
Later among the works it cites.
A survey of deep learning on mobile devices: Applications, optimizations, challenges, and research opportunities
Tianming Zhao, Yucheng Xie, Yan Wang, Jerry Cheng, Xiaonan Guo, Bin Hu, and Yingying Chen. 2022 · 2022
Later among the works it cites.
Optimizing models - Core ML Tools overview
Apple. 2023 · 2023
Later among the works it cites.
The role of interactive visualization in explaining (large) NLP models: From data to inference
Richard Brath, Daniel Keim, Johannes Knittel, Shimei Pan, Pia Sommerauer, and Hendrik Strobelt. 2023 · 2023
Later among the works it cites.
Zeno: An interactive framework for behavioral evaluation of machine learning. In CHI Conference on Human Factors in Computing Systems (Hamburg, Germany). Association for Computing Machinery, New York, NY, USA, 22 pages
Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, and Adam Perer. 2023 · 2023
Later among the works it cites.
MLX: Efficient and flexible machine learning on Apple silicon
Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. 2023 · 2023
Later among the works it cites.
Efficient deep learning: A survey on making deep learning models smaller, faster, and better
Gaurav Menghani. 2023 · 2023
Later among the works it cites.
Visual studio code
Microsoft. 2023 · 2023
Later among the works it cites.
NVIDIA deep learning TensorRT documentation
NVIDIA. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
PyTorch Examples
PyTorch. 2023 · 2023
Later among the works it cites.
The AI index report: Measuring trends in artificial intelligence
Stanford. 2023 · 2023
Later among the works it cites.
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, and Anurag Ranjan. 2023 · 2023
Later among the works it cites.
Data and Network Introspection Kit
Megan Maher Welsh, David Koski, Miguel Sarabia, Niv Sivakumar, Ian Arawjo, Aparna Joshi, Moussa Doumbouya, Luca Suau, Xavierand Zappella, and Nicholas Apostoloff. 2023 · 2023
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.
Model compression in practice: Lessons learned from practitioners creating on-device machine learning experiences. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM
Fred Hohman, Mary Beth Kery, Donghao Ren, and Dominik Moritz. 2024 · 2024
Closest in time.