Machine Learning Engineer: 27th May 2025

How to build an Approximate Nearest Neighbor Search System on top of Object Storage (adriacabeza.github.io, 2025-05-24). Explores building an online anomaly detection system using SPANN for vector search in object storage, emphasizing efficient data representation, clustering, and dynamic pruning techniques for effective querying and reduced memory usage

My very busy week (allendowney.com, 2025-05-22). Allen Downey shares insights from a hectic week presenting at ODSC and PyConUS, covering topics in Bayesian modeling with PyMC, time series analysis with StatsModels, and analyzing survey data with Pandas

Computing Hessian Matrix Via Automatic Differentiation (leimao.github.io, 2025-05-22). Learn how to compute the Hessian matrix using automatic differentiation tools like PyTorch and TensorFlow, focusing on mathematical principles, the Jacobian matrix, and the relationship between gradients and higher-order derivatives

Data Sources (blog.raymond.burkholder.net, 2025-05-24). This study employs an interpretable machine learning framework using FinBERT on GDELT news data to predict financial returns, achieving high Sharpe ratios and CAGRs through sentiment analysis and XGBoost classification

Prototyping Gradient Descent in Machine Learning (towardsdatascience.com, 2025-05-24). Explore iterative optimization via Gradient Descent, MSE minimization in linear regression, and data preprocessing methodologies for predicting credit card transactions utilizing Batch Gradient Descent

A simple search engine from scratch (bernsteinbear.com, 2025-05-20). A simple search engine is built using word2vec and cosine similarity to embed and rank blog posts based on their content, demonstrating techniques for text normalization and Python scripting

Distinguishing sets of elliptic curve coefficients (davidlowryduda.com, 2025-05-23). Investigating how many elliptic curve coefficients are needed to uniquely identify an isogeny class using machine learning, point count analysis, and statistical heuristics

Computing and Visualizing Billions of Bohemian Eigenvalues with Python (aetperf.github.io, 2025-05-23). Compute and visualize billions of Bohemian eigenvalues using Python with libraries like Numba, Dask, and Datashader for efficient processing and visualization of complex eigenvalue patterns

Computing Generalized Eigenvalues with LAPACK (eklausmeier.goip.de, 2025-05-20). Learn to compute generalized eigenvalues using C and LAPACK, including installation steps, provided code examples, and output results for specific matrices, leveraging library functions like zggev and zgghrd

Drazin pseudoinverse (johndcook.com, 2025-05-21). The Drazin pseudoinverse generalizes matrix inverses, using Jordan canonical form and allowing for nice behavior with exponents; distinct from the Moore-Penrose pseudoinverse, it has unique properties and applications

Effective graph resistance (johndcook.com, 2025-05-21). The effective resistance in graphs can be computed using the Moore-Penrose pseudoinverse of the graph Laplacian, addressing invertibility challenges and applying linear systems to determine resistance between nodes

Q Day Came Quietly (slow-thoughts.com, 2025-05-22). Jørgen Ellegaard Andersen and Shan Shan's papers reveal exponential speedup in Monte Carlo methods using Gaussian Boson Sampling, significantly enhancing efficiency in high-dimensional integration relevant to finance, quantum chemistry, and machine learning

The Geometry of Intelligence: Why I Think Math Might Hold the Key to Understanding Minds and Machines (novaspivack.com, 2025-05-26). Nova Spivack proposes a geometric framework for understanding intelligence, leveraging concepts like natural gradients and information manifolds to bridge gaps in AI, neuroscience, and consciousness research

Toward a Geometric Theory of Information Processing: A Research Program (novaspivack.com, 2025-05-26). Research proposes a geometric framework for information processing, linking quantum information theory and differential geometry, emphasizing the role of Fisher information metrics in computational efficiency and learning dynamics

Unveil Sources of Uncertainty: Feature Contribution to ConformalPrediction Intervals (freakonometrics.hypotheses.org, 2025-05-20). A novel method combining conformal prediction and cooperative game theory enables uncertainty attribution in machine learning, using Harsanyi allocations and Monte Carlo approximations for improved interpretability and runtime efficiency

Questioning Representational Optimism in Deep Learning (github.com, 2025-05-20). This work challenges representational optimism in deep learning, revealing that evolved networks lack fractured entangled representation (FER), unlike SGD-trained networks, impacting generalization and creativity

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability (arxiv:stat, 2025-05-20). Using the MIMIC-III database, machine learning models like XGBoost predict ICU readmissions in acute pancreatitis patients, leveraging techniques like SHAP analysis and SMOTE for class imbalance, achieving high accuracy and interpretability

MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains (arxiv:cs, 2025-05-20). MultiTab is a benchmark suite for multi-dimensional evaluation of tabular learning algorithms, analyzing 196 datasets with varied characteristics and 13 models, revealing sensitivity to data regimes and enhancing model selection

LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks (arxiv:stat, 2025-05-20). LOBSTUR-GNN leverages local bootstrapping and Canonical Correlation Analysis for hyperparameter tuning in unsupervised Graph Neural Networks, achieving a 65.9% accuracy improvement on classification tasks with no ground-truth labels

A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial Optimization (arxiv:cs, 2025-05-22). FrontierCO benchmark evaluates 16 ML-based solvers, including graph neural networks and LLMs, across eight CO problem types, providing insights for practical applications in large-scale combinatorial optimization

Prime Collective Communications Library -- Technical Report (arxiv:cs, 2025-05-20). Prime Collective Communications Library (PCCL) enhances distributed ML workloads with dynamic peer joining, fault tolerance, efficient all-reduce operations, and optimization strategies using Python bindings compatible with PyTorch, achieving high bandwidth across global networks

Harnessing the Universal Geometry of Embeddings (arxiv.org, 2025-05-21). Explores the universal geometry of embeddings in machine learning, utilizing advanced tools like arXivLabs for community-driven projects and various citation tools for academic insights