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Found 19 papers, 12 papers with code
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
In such datasets, the data dimension can be extremely high, and the underlying data generation process can be unknown and highly nonlinear.
Fast Value Tracking for Deep Reinforcement Learning
Reinforcement learning (RL) tackles sequential decision-making problems by creating agents that interacts with their environment.
A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules
In the new formulation, the discriminator converges to a fixed point while the generator converges to a distribution at the Nash equilibrium.
Non-reversible Parallel Tempering for Deep Posterior Approximation
Notably, in big data scenarios, we obtain an appealing communication cost $O(P\log P)$ based on the optimal window size.
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Sufficient dimension reduction is a powerful tool to extract core information hidden in the high-dimensional data and has potentially many important applications in machine learning tasks.
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.
A Kernel-Expanded Stochastic Neural Network
The deep neural network suffers from many fundamental issues in machine learning.
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration
Deep learning has powered recent successes of artificial intelligence (AI).
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.
Consistent Sparse Deep Learning: Theory and Computation
Deep learning has been the engine powering many successes of data science.
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19.
Computers and Society C.3; H.4; J.3; J.7; K.4; K.6.5
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.
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.
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Bayesian deep learning offers a principled way to address many issues concerning safety of artificial intelligence (AI), such as model uncertainty, model interpretability, and prediction bias.
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 #77 on
Image Classification
on CIFAR-100
(using extra training data)
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters.
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.
A Bayesian Mark Interaction Model for Analysis of Tumor Pathology Images
This statistical methodology not only presents a new model for characterizing spatial correlations in a multi-type spatial point pattern, but also provides a new perspective for understanding the role of cell-cell interactions in cancer progression.
Methodology 62M30, 62F15
