Machine Learning Engineer

Will You Spot the Leaks? A Data Science Challenge (towardsdatascience.com, 2025-05-12). Explore the challenge of identifying data leakage in machine learning, focusing on concepts like target variable leakage, train-test contamination, and techniques such as PCA and OneHotEncoder, while analyzing real-world aviation data

The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help (towardsdatascience.com, 2025-05-08). AutoML tools offer no-code machine learning but may cause architectural risks, like silent data drift and lack of traceability. Tools like scikit-learn and TensorFlow are often bypassed, hindering reliability

Adventures in Imbalanced Learning and Class Weight (andersource.dev, 2025-05-08). Exploration of class weighting in imbalanced learning reveals that conventional inverse proportional strategies often yield inferior results compared to tailored weights; experimentation with metrics like F1 and balanced accuracy informs better model tuning

SDFs and the Fast sweeping algorithm in Jax (rohangautam.github.io, 2025-05-08). Explore the Fast Sweeping Method for solving Eikonal equations efficiently with JAX, including concepts like level sets, signed distance functions, and the approach to interface evolution in computational simulations

ML in Finance (blog.raymond.burkholder.net, 2025-05-08). A combination of deep learning methods, like Transformers for anomaly detection and CNN-LSTM frameworks for stock trading, outperform traditional techniques in financial data analysis and trading strategies

Reinforcement Learning in Trading (blog.quantinsti.com, 2025-05-08). Explore reinforcement learning in trading with Q-learning and experience replay, highlighting its advantages over traditional machine learning methods and its capacity to develop long-term reward-focused strategies

AI, predict a buyout! (sciencespot.co.uk, 2025-05-12). Machine learning, specifically gradient boosting regression trees, Kaplan-Meier survival curves, and AFT models, predict digital economy business longevity, acquisitions, and competition impact, enhancing strategic decision-making for firms and policymakers

Understanding Word Embeddings (1) – Algebra (eranraviv.com, 2025-05-07). Exploring matrix multiplication as a linear transformation reveals the foundational role of one-hot encoding in understanding word embeddings and how these concepts relate to AI models across different domains

Deep Boltzman Machine: Your Gateway to Neural Network Bliss (howtolearnmachinelearning.com, 2025-05-09). Deep Boltzmann Machines utilize principles of statistical mechanics for unsupervised learning, employing techniques like contrastive divergence to model complex data patterns in applications like computer vision and natural language processing

💭 Too much magic – Will McGugan – Will McGugan's essays (waylonwalker.com, 2025-05-10). Exploring 'too much magic' versus 'bad magic' in programming, highlighting the implications of cryptic errors and implementation details in magical solutions like Kubernetes and Grafana dashboards

When Models See Ghosts - Investigating Why Adversarial Examples Break Our Models (boschko.ca, 2025-05-12). Investigates adversarial examples in machine learning, covering manifold hypothesis, the geometry of training data, decision boundaries, and how statistical patterns lead models to misclassification

CHOIR: A principled approach to clustering single-cell data (tomsing1.github.io, 2025-05-11). CHOIR offers a principled method for clustering single-cell data, utilizing random forest classifiers to distinguish sibling clusters based on gene expression, while minimizing overclustering risks through various implemented techniques

Fitting models from noisy heuristic labels (emiruz.com, 2025-05-10). The weak supervision paradigm of data programming uses maximum likelihood estimation to create soft labels from heuristic labeling functions for training binary classification models without requiring true labels

Neural Networks from Maximizing Rate Reduction (fanpu.io, 2025-05-10). Neural networks can be designed by maximizing coding rate reduction, enhancing feature discrimination and model interpretability without backpropagation, focusing on inter-class orthogonality and intra-class diversity in representation learning

Learn you Galois Fields for Great Good (11): Reed-Solomon as Linear Algebra (xorvoid.com, 2025-05-10). Explore Reed-Solomon Erasure Codes through polynomial evaluation, interpolation, and linear algebra techniques like Vandermonde matrices, matrix multiplication, and LU factorization

The Modal Social Science Dataset is Under-Analyzed (gojiberries.io, 2025-05-13). The Modal Social Science Dataset highlights the need for expanded statistical analyses in empirical social science to minimize missed data patterns, utilizing tools like FDR correction and hierarchical shrinkage for validity

Tensor Calculus Layout Conventions (leimao.github.io, 2025-05-08). Tensor calculus layout conventions explored through numerator and denominator layouts, detailing derivative calculations for scalars, vectors, and matrices using systematic unrolling techniques

Plexus: Taming Billion-edge Graphs with 3D Parallel GNN Training (arxiv:cs, 2025-05-07). Plexus introduces a 3D parallel approach for full-graph training, achieving 2.3x-12.5x speedups on billion-edge graphs while optimizing load balancing and reducing training time significantly on GPU clusters

The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion (arxiv:stat, 2025-05-08). Deep learning for conversion prediction in online ads faces challenges like gradient sparsity and overfitting. The Sparse Optimizer and a frequency-adaptive learning rate for embedding tables are evaluated against embedding re-initialization

Solar Flare Forecast: A Comparative Analysis of Machine Learning Algorithms for Solar Flare Class Prediction (arxiv:cs, 2025-05-06). This study compares Random Forest, k-Nearest Neighbors, and XGBoost for solar flare classification using 13 SHARP parameters, with innovative binary and multiclass classification and dimensionality reduction techniques

Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest (arxiv:cs, 2025-05-06). A novel Explainable AI method enhances Isolation Forest's outlier detection by using Decision Predicate Graphs and the Inlier-Outlier Propagation Score to clarify feature contributions and decision boundaries in machine learning models

Machine Learning Workflow for Morphological Classification of Galaxies (arxiv:astro, 2025-05-07). Spherinator utilizes Generative Deep Learning for analyzing petabyte-scale astrophysical simulations, enabling morphological classification of galaxies through a machine-learning workflow that supports scalability and reproducibility using open-source tools aligned with FAIR principles

Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation (arxiv:cs, 2025-05-06). This study introduces a machine learning framework combining feature selection and synthetic data augmentation to enhance classification accuracy and interpretability in omics datasets, using bootstrap analysis on the E MTAB 8026 dataset

Our Experience at ALT 2025 (techblog.criteo.com, 2025-05-07). Criteo participated in ALT 2025 in Milan, presenting research on bandit problems and algorithmic learning, highlighting concepts like randomised exploration and logarithmic regret in decision-making processes, with one paper winning an Outstanding Paper Award

Continuous Thought Machines (pub.sakana.ai, 2025-05-12). The Continuous Thought Machine (CTM) is a novel neural network architecture utilizing neural dynamics and synchronization for complex problem-solving, including real-time maze navigation