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06/01/2023
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model
This paper introduces UnDiff, a diffusion probabilistic model capable of...
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03/06/2023
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
Transfer learning and ensembling are two popular techniques for improvin...
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02/21/2023
Differentiable Rendering with Reparameterized Volume Sampling
In view synthesis, a neural radiance field approximates underlying densi...
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02/10/2023
Star-Shaped Denoising Diffusion Probabilistic Models
Methods based on Denoising Diffusion Probabilistic Models (DDPM) became ...
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12/20/2022
StyleDomain: Analysis of StyleSpace for Domain Adaptation of StyleGAN
Domain adaptation of GANs is a problem of fine-tuning the state-of-the-a...
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11/02/2022
Entropic Neural Optimal Transport via Diffusion Processes
We propose a novel neural algorithm for the fundamental problem of compu...
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10/17/2022
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Domain adaptation framework of GANs has achieved great progress in recen...
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09/08/2022
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
A fundamental property of deep learning normalization techniques, such a...
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04/06/2022
FFC-SE: Fast Fourier Convolution for Speech Enhancement
Fast Fourier convolution (FFC) is the recently proposed neural operator ...
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03/24/2022
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement
Generative adversarial networks have recently demonstrated outstanding p...
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12/29/2021
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
Channel decoding, channel detection, channel assessment, and resource ma...
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11/23/2021
Variational Autoencoders for Studying the Manifold of Precoding Matrices with High Spectral Efficiency
In multiple-input multiple-output (MIMO) wireless communications systems...
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10/28/2021
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Structured latent variables allow incorporating meaningful prior knowled...
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08/31/2021
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Generative adversarial networks (GANs) have an enormous potential impact...
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06/29/2021
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Despite the conventional wisdom that using batch normalization with weig...
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06/15/2021
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Averaging predictions over a set of models – an ensemble – is widely use...
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06/08/2021
Towards Practical Credit Assignment for Deep Reinforcement Learning
Credit assignment is a fundamental problem in reinforcement learning, th...
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07/16/2020
On Power Laws in Deep Ensembles
Ensembles of deep neural networks are known to achieve state-of-the-art ...
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06/30/2020
Involutive MCMC: a Unifying Framework
Markov Chain Monte Carlo (MCMC) is a computational approach to fundament...
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06/18/2020
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Tensor decomposition methods have recently proven to be efficient for co...
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06/11/2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Training neural networks with binary weights and activations is a challe...
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05/14/2020
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
One of the generally accepted views of modern deep learning is that incr...
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05/08/2020
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
The overestimation bias is one of the major impediments to accurate off-...
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03/04/2020
Deterministic Decoding for Discrete Data in Variational Autoencoders
Variational autoencoders are prominent generative models for modeling di...
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02/22/2020
Stochasticity in Neural ODEs: An Empirical Study
Stochastic regularization of neural networks (e.g. dropout) is a wide-sp...
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02/21/2020
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Test-time data augmentation—averaging the predictions of a machine learn...
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02/15/2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertain...
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12/20/2019
MLRG Deep Curvature
We present MLRG Deep Curvature suite, a PyTorch-based, open-source packa...
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11/22/2019
Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
Learning models with discrete latent variables using stochastic gradient...
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11/13/2019
Structured Sparsification of Gated Recurrent Neural Networks
Recently, a lot of techniques were developed to sparsify the weights of ...
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10/29/2019
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
Generative models produce realistic objects in many domains, including t...
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07/17/2019
Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
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06/09/2019
The Implicit Metropolis-Hastings Algorithm
Recent works propose using the discriminator of a GAN to filter out unre...
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05/08/2019
Importance Weighted Hierarchical Variational Inference
Variational Inference is a powerful tool in the Bayesian modeling toolki...
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05/01/2019
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-s...
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04/09/2019
User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks
We propose a novel multi-texture synthesis model based on generative adv...
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02/07/2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...
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12/12/2018
Bayesian Sparsification of Gated Recurrent Neural Networks
Bayesian methods have been successfully applied to sparsify weights of n...
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11/11/2018
ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks
Neural Network is a powerful Machine Learning tool that shows outstandin...
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11/01/2018
Variational Dropout via Empirical Bayes
We study the Automatic Relevance Determination procedure applied to deep...
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10/25/2018
Bayesian Compression for Natural Language Processing
In natural language processing, a lot of the tasks are successfully solv...
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10/16/2018
Metropolis-Hastings view on variational inference and adversarial training
In this paper we propose to view the acceptance rate of the Metropolis-H...
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10/16/2018
The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models
Bayesian inference is known to provide a general framework for incorpora...
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10/11/2018
Pairwise Augmented GANs with Adversarial Reconstruction Loss
We propose a novel autoencoding model called Pairwise Augmented GANs. We...
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10/05/2018
Doubly Semi-Implicit Variational Inference
We extend the existing framework of semi-implicit variational inference ...
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06/26/2018
Conditional Generators of Words Definitions
We explore recently introduced definition modeling technique that provid...
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06/06/2018
Universal Conditional Machine
We propose a single neural probabilistic model based on variational auto...
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03/14/2018
Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
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02/20/2018
Bayesian Incremental Learning for Deep Neural Networks
In industrial machine learning pipelines, data often arrive in parts. Pa...
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02/13/2018