Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Ho, Lam Si Tung"'
We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks wi
Externí odkaz:
http://arxiv.org/abs/2410.22065
Compartmental models, especially the Susceptible-Infected-Removed (SIR) model, have long been used to understand the behaviour of various diseases. Allowing parameters, such as the transmission rate, to be time-dependent functions makes it possible t
Externí odkaz:
http://arxiv.org/abs/2409.17968
1. Abrupt environmental changes can lead to evolutionary shifts in not only mean (optimal value), but also variance of descendants in trait evolution. There are some methods to detect shifts in optimal value but few studies consider shifts in varianc
Externí odkaz:
http://arxiv.org/abs/2312.17480
Autor:
Nguyen, Cuong N., Tran, Phong, Ho, Lam Si Tung, Dinh, Vu, Tran, Anh T., Hassner, Tal, Nguyen, Cuong V.
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationall
Externí odkaz:
http://arxiv.org/abs/2312.00656
Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock
Externí odkaz:
http://arxiv.org/abs/2310.05892
Publikováno v:
Bull.Math.Bio.85.8 (2023) 71
Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the populati
Externí odkaz:
http://arxiv.org/abs/2211.08277
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that
Externí odkaz:
http://arxiv.org/abs/2209.05709
Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology. Therefore, it is crucial to understand when we can estimate the ancestral state accurately. Previous works provide a necessary and
Externí odkaz:
http://arxiv.org/abs/2207.12897
1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. 2. We propose an ensemble variable selection method (R package
Externí odkaz:
http://arxiv.org/abs/2204.06032
In this paper, we study the learning rate of generalized Bayes estimators in a general setting where the hypothesis class can be uncountable and have an irregular shape, the loss function can have heavy tails, and the optimal hypothesis may not be un
Externí odkaz:
http://arxiv.org/abs/2111.10243