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ORCIDAlireza Makhzani
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2020 – today
- 2024
[c18]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. ICML 2024- [c17]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML 2024 - [c16]Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani:
A Computational Framework for Solving Wasserstein Lagrangian Flows. ICML 2024 - [c15]Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Baker Grosse:
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. ICML 2024 - [i19]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR abs/2402.03496 (2024) - [i18]Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger B. Grosse:
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. CoRR abs/2404.17546 (2024) - 2023
- [c14]Kirill Neklyudov
, Rob Brekelmans, Daniel Severo, Alireza Makhzani:
Action Matching: Learning Stochastic Dynamics from Samples. ICML 2023: 25858-25889 - [c13]Daniel Severo, James Townsend, Ashish J. Khisti, Alireza Makhzani:
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding. ICML 2023: 30633-30645 - [c12]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. NeurIPS 2023 - [i17]Juan Carrasquilla, Mohamed Hibat-Allah, Estelle M. Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai:
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition. CoRR abs/2301.08292 (2023) - [i16]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger B. Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. CoRR abs/2303.06992 (2023) - [i15]Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani:
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs. CoRR abs/2305.09705 (2023) - [i14]Kirill Neklyudov, Jannes Nys, Luca A. Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani:
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation. CoRR abs/2307.07050 (2023) - [i13]Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani:
A Computational Framework for Solving Wasserstein Lagrangian Flows. CoRR abs/2310.10649 (2023) - [i12]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR abs/2312.05705 (2023) - 2022
- [j1]Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich:
Compressing Multisets With Large Alphabets. IEEE J. Sel. Areas Inf. Theory 3(4): 605-615 (2022) - [c11]Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich:
Compressing Multisets with Large Alphabets. DCC 2022: 322-331 - [c10]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. ICLR 2022 - [i11]Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani:
Variational Model Inversion Attacks. CoRR abs/2201.10787 (2022) - [i10]Kirill Neklyudov, Daniel Severo, Alireza Makhzani:
Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples. CoRR abs/2210.06662 (2022) - 2021
- [c9]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. ICML 2021: 9136-9147 - [c8]Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani:
Variational Model Inversion Attacks. NeurIPS 2021: 9706-9719 - [i9]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. CoRR abs/2102.11086 (2021) - [i8]Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich:
Compressing Multisets with Large Alphabets. CoRR abs/2107.09202 (2021) - 2020
- [c7]Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse:
Evaluating Lossy Compression Rates of Deep Generative Models. ICML 2020: 4444-4454 - [i7]Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger B. Grosse:
Evaluating Lossy Compression Rates of Deep Generative Models. CoRR abs/2008.06653 (2020) - [i6]Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg:
Likelihood Ratio Exponential Families. CoRR abs/2012.15480 (2020)
2010 – 2019
- 2018
- [i5]Alireza Makhzani:
Implicit Autoencoders. CoRR abs/1805.09804 (2018) - 2017
- [c6]Alireza Makhzani, Brendan J. Frey:
PixelGAN Autoencoders. NIPS 2017: 1975-1985 - [i4]Alireza Makhzani, Brendan J. Frey:
PixelGAN Autoencoders. CoRR abs/1706.00531 (2017) - [i3]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:
StarCraft II: A New Challenge for Reinforcement Learning. CoRR abs/1708.04782 (2017) - 2015
- [c5]Alireza Makhzani, Brendan J. Frey:
Winner-Take-All Autoencoders. NIPS 2015: 2791-2799 - [i2]Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian J. Goodfellow:
Adversarial Autoencoders. CoRR abs/1511.05644 (2015) - 2014
- [c4]Alireza Makhzani, Brendan J. Frey:
k-Sparse Autoencoders. ICLR (Poster) 2014 - [i1]Alireza Makhzani, Brendan J. Frey:
A Winner-Take-All Method for Training Sparse Convolutional Autoencoders. CoRR abs/1409.2752 (2014) - 2013
- [c3]Alireza Makhzani, Shahrokh Valaee:
Distributed spectrum sensing in cognitive radios via graphical models. CAMSAP 2013: 376-379 - 2012
- [c2]Alireza Makhzani, Shahrokh Valaee:
Reconstruction of jointly sparse signals using iterative hard thresholding. ICC 2012: 3564-3568 - 2011
- [c1]Alireza Makhzani, Shahrokh Valaee:
Reconstruction of a Generalized Joint Sparsity Model using Principal Component Analysis. CAMSAP 2011: 269-272
Coauthor Index
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last updated on 2024-10-15 20:46 CEST by the dblp team
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