Machine Learning Engineer: 17th June 2025

Programming tensor cores in Mojo (veitner.bearblog.dev, 2025-06-13). Explore the MMA PTX instruction for programming NVIDIA GPUs using Mojo, with detailed implementations of operations in bfloat16 and float32 formats for AI applications

Models for Leptonic Radiation From Galaxies (johanneslederer.com, 2025-06-11). The study utilizes machine learning techniques to model radiation from blazars, highly radiative galactic objects, showcasing the integration of data science and astrophysics in addressing complex problems

Does Form Really Shape Function? (quantamagazine.org, 2025-06-12). L. Mahadevan discusses the relationship between biological form and function, exploring concepts like morphogenesis and the geometry of structures, including Möbius strips, using tools like gels and physics principles

Impact of Budget Deficits on Treasury Yields with XGBoost (datageeek.com, 2025-06-11). Using XGBoost, the study reveals budget deficits minimally impact Treasury yields, attributed to US economic dominance and its reserve currency status, supported by variable importance analysis

An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters (thierrymoudiki.github.io, 2025-06-14). A comparison between XGBoost's default hyperparameters and GenericBooster + LinearRegression is analyzed for balanced accuracy across 72 datasets, revealing overfitting tendencies and performance discrepancies

Elastic Net Regression Explained with Example and Application (statisticalaid.com, 2025-06-13). Elastic Net Regression combines Ridge and Lasso techniques to manage high-dimensional data and multicollinearity, utilizing Python's scikit-learn for implementation, demonstrating improved accuracy and flexibility in regression modeling

Foundations of Computer Vision (visionbook.mit.edu, 2025-06-15). This text covers foundational topics in computer vision, including image formation, learning algorithms, neural networks, multiscale representations, and challenges in learning-based vision systems, with an emphasis on visual intuition

Cyclical Embedding (dm13450.github.io, 2025-06-16). Cyclical embeddings transform numerical cyclical variables using trigonometric functions, evaluated through Julia in financial contexts like daily trading volumes, demonstrating variable relationships and model performance nuances

Why Bayesian Optimization Can Fall Short for Materials Innovation (citrine.io, 2025-06-11). Bayesian optimization's limitations in materials innovation include computational intensity, high-dimensional search spaces, and the challenge of multi-objective optimization, prompting shifts to alternative methods like random forests for improved scalability and interpretability

Randomized Kaczmarz: How Should You Sample? (ethanepperly.com, 2025-06-16). Randomized Kaczmarz explores sampling strategies for solving consistent linear equations using selection probabilities (uniform vs. standard), examining their convergence rates and the influence of row equilibration on computational efficiency

From Math to Code: Building GAM with Penalty Functions From Scratch (kenkoonwong.com, 2025-06-11). Explore penalized Generalized Additive Models (GAM) through matrix calculus, GCV optimization, and customized GAM function implementation with an emphasis on penalty matrices and B-spline basis functions

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models (freakonometrics.hypotheses.org, 2025-06-13). Exploring cooperative game theory for machine learning interpretability, this work introduces Weber and Harsanyi allocation sets, moving beyond Shapley values to enhance feature attribution methods and their theoretical foundations

No world model, no general AI (richardcsuwandi.github.io, 2025-06-11). Researchers at Google DeepMind demonstrate that agents capable of generalizing tasks require learned world models, using a rigorous framework involving environments, goals, and the extraction of transition functions for predictive modeling

There is Fun in the Fundamentals (bastian.rieck.me, 2025-06-13). The importance of mastering fundamental concepts in machine learning, such as learning theory and graph learning, is highlighted amidst the current emphasis on large language models and the balance between data and inductive biases

Studying inductive biases of random networks via local volumes (blog.eleuther.ai, 2025-06-12). This study explores inductive biases in random neural networks using star domain volume estimates, investigating how initialization affects learning behavior and the relationship between parameter-function maps and generalization

Prediction isn’t understanding: AI’s evolution and the soul of science (firstprinciples.org, 2025-06-11). AI's evolution is traced from expert systems like MYCIN to modern neural networks, emphasizing structured reasoning, pattern recognition, and breakthroughs in deep learning, crucial for scientific discovery beyond mere prediction

More Transformation Based Learning (elijahpotter.dev, 2025-06-13). Elijah Potter discusses transformation-based learning, focusing on its application in POS tagging and nominal phrase chunking, highlighting model optimization and accuracy improvements while addressing the limitations of traditional neural networks

Don’t stop till you get enough – sample size in machine learning (blog.engora.com, 2025-06-13). Sample size in supervised machine learning is critical for model performance. Various approaches, tools like R and Python libraries, and insights from medicine suggest hundreds of thousands of samples may be needed

Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values (arxiv:cs, 2025-06-13). A new Monte Carlo estimator combines linear regression and probabilistic values, achieving superior Shapley value accuracy, outperforming popular methods by at least $2.6 imes$ and improving general estimators by $215 imes$ in error reduction

A new type of federated clustering: A non-model-sharing approach (arxiv:cs, 2025-06-11). DC-Clustering is a novel federated clustering method enabling robust analysis of complex data partitions using k-means or spectral clustering, focusing on privacy and efficiency in sensitive domains like healthcare and finance

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia (arxiv:econ, 2025-06-12). Evaluation of ML models like Extreme Gradient Boosting outperforms ARIMA in predicting Indonesian inflation using payment system and macroeconomic data, highlighting effective variable relationships and significant insights for monetary policy