Alireza Makhzani

According to our database, Alireza Makhzani authored at least 27 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo.

Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Baker Grosse

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Computational Framework for Solving Wasserstein Lagrangian Flows.

Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective.

Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

CoRR, 2023

Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs.

Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani

CoRR, 2023

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition.

Juan Carrasquilla, Mohamed Hibat-Allah, Estelle M. Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai

CoRR, 2023

Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation.

Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Action Matching: Learning Stochastic Dynamics from Samples.

Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

Proceedings of the International Conference on Machine Learning, 2023

One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding.

Daniel Severo, James Townsend, Ashish J. Khisti, Alireza Makhzani

Proceedings of the International Conference on Machine Learning, 2023

2022

Compressing Multisets With Large Alphabets.

Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich

IEEE J. Sel. Areas Inf. Theory, December, 2022

Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples.

Kirill Neklyudov, Daniel Severo, Alireza Makhzani

CoRR, 2022

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds.

Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Variational Model Inversion Attacks.

Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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 J. Maddison

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Likelihood Ratio Exponential Families.

Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg

CoRR, 2020

Evaluating Lossy Compression Rates of Deep Generative Models.

Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse

Proceedings of the 37th International Conference on Machine Learning, 2020

2018

Implicit Autoencoders.

Alireza Makhzani

CoRR, 2018

2017

StarCraft II: A New Challenge for Reinforcement Learning.

Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

CoRR, 2017

PixelGAN Autoencoders.

Alireza Makhzani, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015

Adversarial Autoencoders.

Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow

CoRR, 2015

Winner-Take-All Autoencoders.

Alireza Makhzani, Brendan J. Frey

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

A Winner-Take-All Method for Training Sparse Convolutional Autoencoders.

Alireza Makhzani, Brendan J. Frey

CoRR, 2014

k-Sparse Autoencoders.

Alireza Makhzani, Brendan J. Frey

Proceedings of the 2nd International Conference on Learning Representations, 2014

2013

Distributed spectrum sensing in cognitive radios via graphical models.

Alireza Makhzani, Shahrokh Valaee

Proceedings of the 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013

2012

Reconstruction of jointly sparse signals using iterative hard thresholding.

Alireza Makhzani, Shahrokh Valaee

Proceedings of IEEE International Conference on Communications, 2012

2011

Reconstruction of a Generalized Joint Sparsity Model using Principal Component Analysis.

Alireza Makhzani, Shahrokh Valaee

Proceedings of the 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2011