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Data Party THU
Data Party THU
Apr 12, 2026 · Artificial Intelligence

Physics‑Informed GP Model Enables Near‑Infinite Stability in Hot Molecular Dynamics

Researchers from the University of Manchester introduced a physics‑informed Gaussian Process atomic energy model that, unlike traditional machine‑learning potentials, remains stable in molecular dynamics simulations up to 1000 K for tens of nanoseconds, demonstrating robust force predictions and reliable long‑time behavior across diverse molecules.

Gaussian ProcessMachine Learning Potentialscomputational chemistry
0 likes · 7 min read
Physics‑Informed GP Model Enables Near‑Infinite Stability in Hot Molecular Dynamics
HyperAI Super Neural
HyperAI Super Neural
Mar 31, 2026 · Artificial Intelligence

AI Uncovers 118 New Exoplanets with RAVEN, Achieving 91% Overall Accuracy

A Warwick University team introduced the RAVEN pipeline, which uses synthetic training data and a combined GBDT‑GP model to rank and validate TESS candidates, achieving over 97% AUC on all false‑positive scenarios, 91% overall accuracy on 1,361 external TOIs, and confirming 118 new exoplanets.

AIGBDTGaussian Process
0 likes · 17 min read
AI Uncovers 118 New Exoplanets with RAVEN, Achieving 91% Overall Accuracy
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 29, 2023 · Cloud Computing

MagicScaler: Achieving High QoS and Low Cost with Uncertainty‑Aware Autoscaling

The MagicScaler framework, introduced by Alibaba Cloud’s big‑data engineering team and collaborators, combines a multi‑scale attention Gaussian process predictor with an uncertainty‑aware elastic scaling decision engine, delivering significantly higher quality‑of‑service and lower operational costs than traditional autoscaling methods, as demonstrated on real MaxCompute workloads.

Gaussian ProcessPredictive ModelingResource Management
0 likes · 7 min read
MagicScaler: Achieving High QoS and Low Cost with Uncertainty‑Aware Autoscaling
Volcano Engine Developer Services
Volcano Engine Developer Services
Dec 15, 2022 · Artificial Intelligence

How Adaptive Transfer Kernels Boost Low‑Resource Regression: IEEE TPAMI Insights

The paper introduces adaptive transfer kernel learning for transfer Gaussian process regression, defines transfer kernels mathematically, proposes three generalized forms and two improved kernels, proves their positive‑semi‑definiteness, and demonstrates superior performance on low‑resource regression tasks through extensive experiments.

Gaussian Processkernel methodslow-resource regression
0 likes · 9 min read
How Adaptive Transfer Kernels Boost Low‑Resource Regression: IEEE TPAMI Insights
Alimama Tech
Alimama Tech
Sep 8, 2021 · Artificial Intelligence

Deep Uncertainty-Aware Learning (DUAL) for Click‑Through Rate Prediction and Exploration Strategies

The paper presents Deep Uncertainty‑Aware Learning (DUAL), a scalable Bayesian deep‑learning framework that combines a neural feature extractor with a Gaussian‑process prior to model CTR prediction uncertainty, mitigates feedback‑loop bias, and enables confidence‑driven exploration (UCB and Thompson sampling) that improves long‑term utility while preserving accuracy.

Gaussian ProcessUncertainty Modelingcontextual bandits
0 likes · 15 min read
Deep Uncertainty-Aware Learning (DUAL) for Click‑Through Rate Prediction and Exploration Strategies
Efficient Ops
Efficient Ops
Jul 17, 2021 · Databases

How AutoTiKV’s Machine Learning Optimizes Beaver Search Engine Performance

This article describes how the Beaver search engine’s many performance‑related configuration parameters can be automatically tuned using machine‑learning techniques from OtterTune and AutoTiKV, detailing the background research, Gaussian Process regression model, Bayesian optimization process, implementation steps, test results, and future improvements.

Bayesian OptimizationBeaverDatabase Performance
0 likes · 23 min read
How AutoTiKV’s Machine Learning Optimizes Beaver Search Engine Performance
Hulu Beijing
Hulu Beijing
Mar 28, 2019 · Artificial Intelligence

Mastering Bayesian Hyperparameter Optimization: A Practical Guide

This article explains what hyper‑parameters are, why their tuning is a black‑box problem, and how Bayesian optimization—using surrogate models, acquisition functions, and posterior inference—offers a more efficient alternative to grid or random search, while also listing popular open‑source tools and discussing its limitations.

Acquisition FunctionAutoMLBayesian Optimization
0 likes · 8 min read
Mastering Bayesian Hyperparameter Optimization: A Practical Guide