Alireza Makhzani
Home Group Publications
  • Compressing multisets with large alphabets
    Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
    IEEE Journal on Selected Areas in Information Theory, Special Issue on Modern Compression, 2023
  • Action Matching: Learning Stochastic Dynamics from Samples
    Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
    arXiv:2210.06662, 2023
  • Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
    Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack, Alireza Makhzani, Kirill Neklyudov, Graham Taylor, Giacomo Torlai
    arXiv:2301.08292, 2023
  • Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
    Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver, Roger Grosse, Alireza Makhzani
    International Conference on Learning Representations (ICLR), 2022
  • Variational Model Inversion Attacks
    Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani
    Advances in Neural Information Processing Systems (NeurIPS), 2021
  • Your Dataset is a Multiset and You Should Compress it Like One
    Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
    Advances in Neural Information Processing Systems (NeurIPS) Workshop on Deep Generative Models and Downstream Applications, 2021
  • Few Shot Image Generation via Implicit Autoencoding of Support Sets
    Andy Huang, Kuan-Chieh Wang, Guillaume Rabusseau, Alireza Makhzani
    Advances in Neural Information Processing Systems (NeurIPS) Workshop on Meta-Learning, 2021
  • Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
    Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison
    International Conference on Machine Learning (ICML), 2021
  • Likelihood ratio exponential families
    Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Steeg
    Advances in Neural Information Processing Systems (NeurIPS) Workshop on Deep Learning through Information Geometry, 2020
  • Evaluating Lossy Compression Rates of Deep Generative Models
    Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse
    International Conference on Machine Learning (ICML), 2020
  • Implicit autoencoders
    Alireza Makhzani
    arXiv:1805.09804, 2018
  • Starcraft II: A new challenge for reinforcement learning
    Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, others
    arXiv:1708.04782, 2017
  • Pixelgan autoencoders
    Alireza Makhzani, Brendan Frey
    Advances in Neural Information Processing Systems (NeurIPS), 2017
  • Adversarial Autoencoders
    Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey
    International Conference on Learning Representations (ICLR) Workshop, 2016
  • Winner-take-all autoencoders
    Alireza Makhzani, Brendan Frey
    Advances in Neural Information Processing Systems (NeurIPS), 2015
  • K-sparse autoencoders
    Alireza Makhzani, Brendan Frey
    International Conference on Learning Representations (ICLR), 2014
  • Distributed spectrum sensing in cognitive radios via graphical models
    Alireza Makhzani, Shahrokh Valaee
    5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013
  • Reconstruction of jointly sparse signals using iterative hard thresholding
    Alireza Makhzani, Shahrokh Valaee
    IEEE International Conference on Communications (ICC), 2012
  • Reconstruction of a generalized joint sparsity model using principal component analysis
    Alireza Makhzani, Shahrokh Valaee
    IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011
  • Alireza Makhzani
  • Vector Institute
  • University of Toronto
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