Evolving Erlang/OTP Feature by Feature - Selectable Features in OTP25
Explore Erlang/OTP's new selectable features, including Value-Based Error Handling. Learn how to evolve the language without affecting existing code, enabling experimentation with backwards-incompatible changes.
Quantum-Inspired Annealing: The State of Play
Explore quantum-inspired annealing's current state, comparing quantum and classical approaches, and examining benchmarks for various optimization problems.
The Data Minimization Principle in Machine Learning
Explore data minimization in machine learning, its challenges, and practical implementations. Examine optimization-based formalization and applications in high-stakes domains, revealing insights on privacy and efficiency.
Configuration-Aware Performance Analysis for High-Performance Computing Frameworks
Explore configuration-aware performance analysis for HPC frameworks, focusing on feature selection impact and identifying performance regressions across software releases.
Nonlinear Statistical Modeling for High-Dimensional and Small Sample Data
Explore nonlinear feature selection methods for high-dimensional, small sample data. Learn about HSIC Lasso, kernel PSIs, and feature selection networks for advanced machine learning applications.
Feature Selection and Clustering with a Quantum Annealer - QuantumCLEF 2024
Dive into quantum annealing techniques for feature selection and clustering through hands-on demonstrations and practical implementations for advanced data analysis tasks.
Discovery in High Dimensions - 2008
Explore statistical methods for uncovering hidden structures in high-dimensional data from various fields, including genetics, astronomy, and consumer behavior.