Search Results for author: Alireza Makhzani

Found 16 papers, 14 papers with code

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

1 code implementation6 Jul 2023 Kirill Neklyudov, Jannes Nys, Luca Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

One of the common computational approaches to this problem is Quantum Variational Monte Carlo (QVMC), in which ground-state solutions are obtained by minimizing the energy of the system within a restricted family of parameterized wave functions.

Variational Monte Carlo

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

1 code implementation16 May 2023 Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani

We present a one-shot method for compressing large labeled graphs called Random Edge Coding.

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds

1 code implementation ICLR 2022 Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Grosse, Alireza Makhzani

Since accurate estimation of MI without density information requires a sample size exponential in the true MI, we assume either a single marginal or the full joint density information is known.

Mutual Information Estimation

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition

2 code implementations19 Jan 2023 Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai

Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.

Combinatorial Optimization

Action Matching: Learning Stochastic Dynamics from Samples

1 code implementation13 Oct 2022 Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani

Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling.

Colorization Super-Resolution

Variational Model Inversion Attacks

1 code implementation NeurIPS 2021 Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani

In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.

Compressing Multisets with Large Alphabets using Bits-Back Coding

1 code implementation15 Jul 2021 Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich

Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings.

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

1 code implementation ICLR Workshop Neural_Compression 2021 Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison

Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.

Data Compression

Likelihood Ratio Exponential Families

no code implementations NeurIPS Workshop DL-IG 2020 Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg

The exponential family is well known in machine learning and statistical physics as the maximum entropy distribution subject to a set of observed constraints, while the geometric mixture path is common in MCMC methods such as annealed importance sampling.


Evaluating Lossy Compression Rates of Deep Generative Models

2 code implementations ICML 2020 Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse

The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge.

Implicit Autoencoders

no code implementations ICLR 2019 Alireza Makhzani

In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions.

Clustering Image-to-Image Translation +1

PixelGAN Autoencoders

1 code implementation NeurIPS 2017 Alireza Makhzani, Brendan Frey

In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.

Unsupervised Image Classification Unsupervised MNIST

Adversarial Autoencoders

28 code implementations18 Nov 2015 Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, Brendan Frey

In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.

Clustering Data Visualization +5

Winner-Take-All Autoencoders

1 code implementation NeurIPS 2015 Alireza Makhzani, Brendan Frey

In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion.

k-Sparse Autoencoders

3 code implementations19 Dec 2013 Alireza Makhzani, Brendan Frey

Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks.

Classification Denoising +1

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