no code implementations • ICML 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
no code implementations • 28 Feb 2024 • Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
1 code implementation • 9 Dec 2023 • Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
Second-order methods for deep learning -- such as KFAC -- can be useful for neural net training.
1 code implementation • 16 Oct 2023 • Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry ($\textit{kinetic energy}$), and the regularization of density paths ($\textit{potential energy}$).
2 code implementations • 19 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.
1 code implementation • 13 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.
1 code implementation • 26 Nov 2021 • Kirill Neklyudov, Priyank Jaini, Max Welling
We accomplish this by viewing the evolution of the modeling distribution as (i) the evolution of the energy function, and (ii) the evolution of the samples from this distribution along some vector field.
1 code implementation • 18 Jun 2021 • Kirill Neklyudov, Roberto Bondesan, Max Welling
Deterministic dynamics is an essential part of many MCMC algorithms, e. g.
1 code implementation • 15 Oct 2020 • Kirill Neklyudov, Max Welling
Markov Chain Monte Carlo (MCMC) algorithms ubiquitously employ complex deterministic transformations to generate proposal points that are then filtered by the Metropolis-Hastings-Green (MHG) test.
no code implementations • 30 Jun 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.
1 code implementation • NeurIPS 2019 • Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov
For any implicit probabilistic model and a target distribution represented by a set of samples, implicit Metropolis-Hastings operates by learning a discriminator to estimate the density-ratio and then generating a chain of samples.
no code implementations • ICLR 2019 • Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov, Dmitry Vetrov
From this point of view, the problem of constructing a sampler can be reduced to the question - how to choose a proposal for the MH algorithm?
2 code implementations • ICLR 2019 • Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging.
1 code implementation • 13 Feb 2018 • Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov
In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation.
5 code implementations • NeurIPS 2017 • Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e. g. removes neurons and/or convolutional channels in CNNs.