Transfer learningIn this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful so
Transfer learning in generalized linear model (xi,yi)∈Rp×{0,1},i=1,…,n y|xi∼P(y|xi)=ρ(y)exp{yxiTβ−b(xiTβ)}. ɛɛɛg(Y ) = Xβ + ɛ,E(ɛ)=0,Var(ɛ)=σ2Σ b′(.)=g−1(.)=E(y|xi) ...
(2023). A linear adjustment based approach to posterior drift in transfer learning. Biometrika, asad029. Liu, R., Li, K., & Shang, Z. (2020). A computationally efficient classification algorithm in posterior drift model: Phase transition and minimax adaptivity. arXiv preprint arXiv:2011.04147...
不同的是该论文考虑优化分位数回归问题(quantile loss function)下的transfer learning。其中source domain和target domain的quantile regression model 定义为: \beta_\tau 是分位数水平\tau下的回归系数 \tau . quantile regression 的loss function 被定义为: 其中\rho_\tau = u(τ − I(u ≤ 0))。 该...
One-shot learning One-shot learning refers to a classification task where the model is provided with only one or a few examples to learn from, and it is then expected to classify a larger number of new examples in the future. This scenario often arises in face recognition, where the model...
Aiming to find advanced SX superalloys applied at 1200 °C, the proposed transfer learning-based model guides us to design a superalloy with a verified creep rupture life of ~170 h at 80 MPa, which exceeds the state-of-art value by 30%. The improved γ/γ′ interface strengthening...
We focus on geospatial transfer-learning problems in which the conditional distribution of y given x varies smoothly in the task variables t that represent spatial coordinates, or both space and time. In this case, T ⊂ Rd is a continuous-valued space. We model p(ω) using a zero-mean ...
1.1. General Transfer Learning (普通迁移学习)1.1.1. Theory (理论)20191008 CVPR-19 Characterizing and Avoiding Negative Transfer Characterizing and avoid negative transfer 形式化并提出如何避免负迁移 20190301 ALT-19 A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation A new criter...
learning architecture (crisprHAL) that can be trained on existing datasets, that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data, and that can generalize predictions to different bacteria. The crisprHAL model ...
Transfer learning Machine learning Data science Unconventional Oil and gas 1. Introduction 1.1. Background During oil field development, the commonly used methods of analysis and decision making include data processing, model building, simulation, forecasting, history matching or calibration, and optimizati...