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Found 8 papers, 2 papers with code
Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks
Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world.
Neural Tangent Kernel of Matrix Product States: Convergence and Applications
In this work, we study the Neural Tangent Kernel (NTK) of Matrix Product States (MPS) and the convergence of its NTK in the infinite bond dimensional limit.
Representation Theorem for Matrix Product States
We study the relation between MPS and neural networks and show that the MPS with a scale-invariant sigmoidal function is equivalent to a one-hidden-layer neural network equipped with a kernel function.
Infinitely Wide Tensor Networks as Gaussian Process
It is known that by introducing appropriate prior to the weights of the neural networks, Gaussian Process can be obtained by taking the infinite-width limit of the Bayesian neural networks from a Bayesian perspective.
The Bayesian Method of Tensor Networks
By Bayes rule, the external information (prior distribution) and the internal information (training data likelihood) are combined coherently, and the posterior distribution and the posterior predictive (marginal) distribution obtained by Bayes rule summarize the total information needed in the inference and prediction, respectively.
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model
Gibbs sampling is a widely used Markov chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences.
Computation Distributed, Parallel, and Cluster Computing
Asynchronous Gibbs Sampling
We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property.
Computation
