Analytics Insight

Artificial Intelligence: Principles and Techniques, Stanford University

Stanford University is offering an online graduate course Artificial Intelligence: Principles and Techniques to provide students with complete knowledge of curr ...
Frontiers

AI for the Next Generation of Particle Physics Experiments

The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...
Insurance Insider

Understanding actuarial reserving in the P&C insurance market

Discover actuarial reserving in P&C insurance—from methods to innovations—enhancing stability and strategy. ✓ Subscribe for ...
Scientific Research Publishing

Mohammad-Djafari, A. (2021) Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems. Entropy, 23, Article 1673.

ABSTRACT: Glioblastoma multiforme (GBM) remains one of the most aggressive brain malignancies, with a median survival of less than 15 months. This study advances glioblastoma multiforme (GBM) survival ...
The Next Platform

Is Nvidia Assembling The Parts For Its Next Inference Platform?

No, we did not miss the fact that Nvidia did an “acquihire” of AI accelerator and system startup and rival Groq on Christmas Eve. But, because our family was traveling on Christmas Day and The Next ...
IEEE

FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing

Abstract: In scenarios with limited training data or where explainability is crucial, conventional neural network-based machine learning models often face challenges. In contrast, Bayesian ...
Fred Hutch

Statistical Learning and Data Science Hub

Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...
VentureBeat

New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...
usace.army.mil

Army establishes new AI, machine learning career path for officers

WASHINGTON – The U.S. Army has established a new career pathway for officers to specialize in artificial intelligence and machine learning (AI/ML), formally designating the 49B AI/ML Officer as an ...
eLife

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
Scientific Research Publishing

Hill, J.L. (2011) Bayesian Nonparametric Modeling for Causal Inference. Journal of Computational and Graphical Statistics, 20, 217-240.

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...
GitHub

mohansree14/Bayesian-Deep-Learning-for-Probabilistic-Financial-Forecasting

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...