- Action matching: learning stochastic dynamics from samples
Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani
International Conference on Machine Learning (ICML), 2023 - Random edge coding: one-shot bits-back coding of large labeled graphs
Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani
International Conference on Machine Learning (ICML), 2023 - Compressing multisets with large alphabets using bits-back coding
Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
IEEE Journal on Selected Areas in Information Theory, Special Issue on Modern Compression, 2023, Also presented in Data Compression Conference, 2021, (Oral Talk) - 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 (Submitted to Quantum), 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, (Best Paper Award) - 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, (Long Talk) - 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, Also presented in NeurIPS Workshop on Bayesian Deep Learning, 2019, (Contributed Talk) - 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