PNAS

Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization

In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
marktechpost

How to Build an End-to-End Production Grade Machine Learning Pipeline with ZenML, Including Custom Materializers, Metadata Tracking, and Hyperparameter Optimization

In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML. We begin by setting up the environment and initializing a ZenML project, then ...
marktechpost

How to Build an Autonomous Machine Learning Research Loop in Google Colab Using Andrej Karpathy’s AutoResearch Framework for Hyperparameter Discovery and Experiment Tracking

In this tutorial, we implement a Colab-ready version of the AutoResearch framework originally proposed by Andrej Karpathy. We build an automated experimentation pipeline that clones the AutoResearch ...
Nature

Quantum annealing for combinatorial optimization: a benchmarking study

Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods.
GitHub

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

SMAC offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing hyperparameter configurations for their (Machine Learning) algorithms, datasets ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
Business Standard

Python Developers Are Turning to AI for Techies to Stay Ahead in AI

New Delhi [India], December 25: Artificial intelligence is no longer a futuristic concept, it's a rapidly growing force shaping industries, economies, and our everyday lives. From personalized ...
Frontiers

Editorial: Emerging trends in large-scale data analysis for neuroscience research

Neuroscience has witnessed a surge in data generation due to advancements in experimental techniques like electrophysiology, imaging, and genomics. To gain deeper insights into the brain's structure ...
IEEE

Learning Nonlinear Dynamics Using Kalman Smoothing

Abstract: Identifying Ordinary Differential Equations (ODEs) from measurement data requires both fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data. The ...
IEEE

Remaining Useful Life Estimation of Lithium-Ion Batteries Via Hyperparameter Optimized Bi-Long Short-Term Memory Recurrent Neural Networks

Abstract: Remaining useful life (RUL) estimation of Lithium-ion batteries (LIBs) is essential to assess their long-term reliability. RUL can enable the prediction of LIB failure and thereby can ...