Zobrazeno 1 - 10
of 1 662
pro vyhledávání: '"DRAPER, DAVID"'
Autor:
Guo, Erdong, Draper, David
Markov Chain Monte Carlo (MCMC), and Tensor Networks (TN) are two powerful frameworks for numerically investigating many-body systems, each offering distinct advantages. MCMC, with its flexibility and theoretical consistency, is well-suited for simul
Externí odkaz:
http://arxiv.org/abs/2409.04729
Autor:
Draper, David, Guo, Erdong
In this discussion note, we respond to the fascinating paper "Martingale Posterior Distributions" by E. Fong, C. Holmes, and S. G. Walker with a couple of comments. On the basis of previous research, a theorem is stated regarding the relationship bet
Externí odkaz:
http://arxiv.org/abs/2302.07779
Autor:
Draper, David A., Bramhall, Bobby
Publikováno v:
Judges' Journal. Fall2024, Vol. 63 Issue 4, p16-20. 5p.
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. Howev
Externí odkaz:
http://arxiv.org/abs/2212.13621
Autor:
Guo, Erdong, Draper, David
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. We prove that the NTK of MPS asymptotically converges to a constant matrix during the gradien
Externí odkaz:
http://arxiv.org/abs/2111.14046
The standard method to check for the independence of two real-valued random variables -- demonstrating that the bivariate joint distribution factors into the product of its marginals -- is both necessary and sufficient. Here we present a simple neces
Externí odkaz:
http://arxiv.org/abs/2111.14040
Autor:
Draper, David, Guo, Erdong
The \textit{Central Limit Theorem (CLT)} is at the heart of a great deal of applied problem-solving in statistics and data science, but the theorem is silent on an important implementation issue: \textit{how much data do you need for the CLT to give
Externí odkaz:
http://arxiv.org/abs/2111.12267
Autor:
Guo, Erdong, Draper, David
In this work, we investigate the universal representation capacity of the Matrix Product States (MPS) from the perspective of boolean functions and continuous functions. We show that MPS can accurately realize arbitrary boolean functions by providing
Externí odkaz:
http://arxiv.org/abs/2103.08277
Autor:
Guo, Erdong, Draper, David
Gaussian Process is a non-parametric prior which can be understood as a distribution on the function space intuitively. It is known that by introducing appropriate prior to the weights of the neural networks, Gaussian Process can be obtained by takin
Externí odkaz:
http://arxiv.org/abs/2101.02333
Autor:
Guo, Erdong, Draper, David
Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By Bayes rule, th
Externí odkaz:
http://arxiv.org/abs/2101.00245