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Found 16 papers, 8 papers with code
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions.
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
In this paper, we focus on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware, as a representative case to study the adversarial attack methods in such adversarial settings.
On Convergence of Federated Averaging Langevin Dynamics
Important to our approach is that the communication efficiency does not deteriorate with the injected noise in the Langevin algorithms.
Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning
Parallel tempering (PT), also known as replica exchange, is the go-to workhorse for simulations of multi-modal distributions.
Information Directed Sampling for Sparse Linear Bandits
Stochastic sparse linear bandits offer a practical model for high-dimensional online decision-making problems and have a rich information-regret structure.
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in big data statistics.
An adaptive Hessian approximated stochastic gradient MCMC method
The bias introduced by stochastic approximation is controllable and can be analyzed theoretically.
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration.
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms.
Ranked #65 on
Image Classification
on CIFAR-100
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications
The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
Ranked #2 on
Click-Through Rate Prediction
on Criteo
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors.
Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation
We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables.
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications
We propose to use deep neural networks to automate the SA process.
Adaptive low rank and sparse decomposition of video using compressive sensing
We address the problem of reconstructing and analyzing surveillance videos using compressive sensing.
