Abstract: Neural networks that overlook the underlying causal relationships among observed variables pose significant risks in high-stakes decision-making contexts due to concerns about the robustness ...
You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
Dharmesh Tailor, Alvaro H.C. Correia, Eric Nalisnick and Christos Louizos. "Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence." [ICLR2025] Qualcomm AI ...
physics_informed_neural_network/ ├── app/ # FastAPI application │ ├── __init__.py │ ├── api/ # API endpoints │ │ ├── __init__.py ...
Subclinical mastitis in dairy cows carries substantial economic, animal welfare, and biosecurity implications. The identification of subclinical forms of the disease is routinely performed through the ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...
The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...
Abstract: Deep neural networks (DNNs) are powerful tools with exceptional approximation capabilities across various applications. However, there is still a lack of substantial progress in attaining ...
Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results