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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark


Jun 23, 2022
Hendrik Borras, Giuseppe Di Guglielmo, Javier Duarte, Nicolò Ghielmetti, Ben Hawks, Scott Hauck, Shih-Chieh Hsu, Ryan Kastner, Jason Liang, Andres Meza, Jules Muhizi, Tai Nguyen, Rushil Roy, Nhan Tran, Yaman Umuroglu, Olivia Weng, Aidan Yokuda, Michaela Blott

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* 15 pages, 7 figures, Contribution to 3rd Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware (MLBench) at 5th Conference on Machine Learning and Systems (MLSys) 

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Designing Counterfactual Generators using Deep Model Inversion


Oct 05, 2021
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias

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* Neurips 2021 

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Training Stacked Denoising Autoencoders for Representation Learning


Feb 16, 2021
Jason Liang, Keith Kelly

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Population-Based Training for Loss Function Optimization


Feb 11, 2020
Jason Liang, Santiago Gonzalez, Risto Miikkulainen

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Evolutionary Neural AutoML for Deep Learning


Apr 09, 2019
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, Risto Miikkulainen

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* 8 pages 

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Evolutionary Architecture Search For Deep Multitask Networks


Apr 17, 2018
Jason Liang, Elliot Meyerson, Risto Miikkulainen

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Evolving Deep Neural Networks


Mar 04, 2017
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, Babak Hodjat

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