Machine Learning Engineer: 1st April 2025

Low responsiveness of ML models to critical or deteriorating health conditions (nature.com, 2025-03-26). Machine learning models underperform in detecting critical health conditions, such as in-hospital mortality and cancer survival, revealing serious deficiencies in responsiveness. Comprehensive testing methods, including gradient ascent and neural activation mapping, are developed for assessment

Asset price movement prediction using empirical mode decomposition and Gaussian mixture models (arxiv:cs, 2025-03-26). Empirical Mode Decomposition and Gaussian Mixture Models are applied to optimize trading decisions in GameStop, Tesla, and XRP markets, enhancing prediction accuracy for hourly price movement with machine learning algorithms like Random Forest and XGBoost

Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data (arxiv:stat, 2025-03-27). Machine learning models for all-cause in-hospital mortality prediction, utilizing comprehensive feature engineering, achieved high performance with Random Forest (AUC 0.94), highlighting its potential for clinical decision support tools

Optimizing ML training with metagradient descent (arxiv.org, 2025-03-25). This research proposes metagradient descent as a novel optimization technique for machine learning training, enhancing efficiency compared to traditional methods, and highlights the contributions from the Simons Foundation and other key institutions

A Taxonomy of Adversarial Machine Learning Attacks and Mitigations (schneier.com, 2025-03-27). NIST has released a comprehensive taxonomy detailing adversarial machine learning attacks and their countermeasures, providing insights into categories of attacks and potential mitigations relevant to security and technology sectors

An Efficient Training Algorithm for Models with Block-wise Sparsity (arxiv:cs, 2025-03-27). Proposes an efficient training algorithm for block-wise sparse weight matrices in machine learning, significantly reducing computation and memory costs without performance loss, while optimizing block size during training

FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies (arxiv:cs, 2025-03-26). FastFT introduces a framework for feature transformation that employs performance prediction, novelty evaluation, and a prioritized memory buffer, enhancing exploration strategies and improving effectiveness in complex data engineering tasks

Investigating the Duality of Interpretability and Explainability in Machine Learning (arxiv:cs, 2025-03-27). Investigates the duality of interpretability and explainability in ML, emphasizing hybrid learning methods that integrate symbolic knowledge into neural networks to replace opaque black box models in critical decision-making

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance (arxiv:stat, 2025-03-27). An Explainable Boosting Machine (EBM) is introduced for predicting car insurance claim frequency and severity, leveraging Generalized Additive Models and cyclic gradient boosting while providing clear interpretability compared to traditional models like GLM and CART

Some Doodles I'm Proud of -- The Capping Algorithm for Embedded Graphs (grossack.site, 2025-03-30). An exploration of the Capping Algorithm for transforming a graph embedded in a surface into a 2-cell embedding, emphasizing its utility and showcasing illustrative drawings to facilitate understanding

Notes on implementing Attention (eli.thegreenplace.net, 2025-03-26). Implement attention blocks in Python using Numpy, focusing on scaled self-attention, batched self-attention, and multi-head attention with detailed shape explanations and necessary functions like Softmax

Looking at Your Data (johndcook.com, 2025-03-29). Wayne Joubert emphasizes the importance of examining data in a machine learning context, utilizing tools like Jupyter notebooks and Matplotlib, while cautioning against bias and data leakage

Attractors in Neural Network Circuits: Beauty and Chaos (towardsdatascience.com, 2025-03-25). Explore the concept of attractors in neural networks, utilizing feedback loops to create complex dynamical behaviors, and implement a simple one-layer NN architecture using tools like NumPy for visualizing attractor dynamics

Matrix Profiles (aneksteind.github.io, 2025-03-26). Matrix profiles aid in time series analysis, enabling anomaly detection and segmentation through algorithms like FLUSS, which identifies self-similar regions in data using a matrix profile for enhanced insights and visualization

Building Blocks of Bias-Variance Tradeoff for Trading the Financial Markets (blog.quantinsti.com, 2025-03-28). Explore the bias-variance tradeoff in machine learning for trading, emphasizing underfitting, overfitting, and error decomposition using regression models and Python tools for algorithmic trading

A New Proof Smooths Out the Math of Melting (quantamagazine.org, 2025-03-31). A new proof confirms the multiplicity-one conjecture regarding singularities in mean curvature flow, enhancing mathematical understanding of surface evolution, with implications in geometry and topology

Matrix Calculus (For Machine Learning and Beyond) (arxiv.org, 2025-03-29). Matrix calculus concepts are explored for their application in machine learning and related fields, focusing on mathematical frameworks essential for understanding advanced computational techniques

Calabi–Yau Manifold (beuke.org, 2025-03-29). Calabi-Yau manifolds are Ricci-flat, complex geometric shapes integral to string theory, featuring intricate topology, special holonomy, and mathematical properties crucial to unifying gravity with fundamental forces

Let the polynomial monster free (alexshtf.github.io, 2025-03-27). Exploring overparametrized polynomial regression, this post discusses concepts like double descent, the impact of polynomial bases, and showcases the use of Bernstein polynomials in machine learning contexts

The Mean-ing of Loss Functions (jiha-kim.github.io, 2025-03-26). Explore the intuition behind loss functions, focusing on squared error in regression, its geometric interpretation as a projection, and the role of conditional expectation in minimizing expected squared error