Machine Learning Engineer: 22nd April 2025

Linkage (11011110.github.io, 2025-04-15). Explores exchange rates between CS conferences, productivity equivalence in machine learning publications, mathematical modeling errors in tariffs, and issues like AI bot crawlers impacting open access in scholarly publishing

#248 Pedro Domingos: How Connectionism Is Reshaping the Future of Machine Learning (aneyeonai.libsyn.com, 2025-04-17). Pedro Domingos discusses Connectionism and its impact on neural networks, highlighting the evolution from 1940s neural networks to transformers, the significance of Backpropagation, and the challenges faced by reinforcement and unsupervised learning

Reproducing Hacker News writing style fingerprinting (antirez.com, 2025-04-16). The post explores Hacker News writing style fingerprinting using cosine similarity and the Burrows-Delta method, focusing on Redis vector sets and data processing techniques with a dataset of 10GB of Hacker News comments

Mixture of Experts (nonint.com, 2025-04-18). Mixture of Experts (MoE) Transformers replace traditional MLP layers with MoE layers, enhancing compute efficiency through sparsity while facing challenges in low-latency and memory-bound environments during inference

Sparsely-gated Mixture Of Experts (MoE) (eli.thegreenplace.net, 2025-04-18). Sparsely-gated Mixture of Experts (MoE) architecture enhances transformer efficiency by enabling selective expert usage based on token relevance, utilizing techniques like top-k selection and softmax scoring

Microsoft’s “1‑bit” AI model runs on a CPU only, while matching larger systems (pappp.net, 2025-04-18). Microsoft's new '1-bit' AI model utilizes ternary weights (-1, 0, 1) to enhance computational efficiency, enabling effective performance on standard CPUs, while drastically reducing memory requirements compared to traditional 16- or 32-bit models

Teaching Myself Math and ML: Attempt #2 (julian.bearblog.dev, 2025-04-16). Julian outlines his renewed journey in self-learning math and machine learning, using resources like Spivak's Calculus, Axler's Linear Algebra, and Andrew Ng's course, while emphasizing problem-solving and personal projects

Mathematical Genealogy (dustysturner.com, 2025-04-17). Dusty Turner explores his academic lineage via the Mathematics Genealogy Project, employing R libraries like tidyverse and igraph, and using ChatGPT to help create a genealogy visualization script

Models Are Just Data Interpolators (justinmath.com, 2025-04-18). Models are primarily data interpolators, and their effectiveness hinges on the quality and representation of the data, rather than on model complexity when facing interpolation and extrapolation challenges

The Statistical Foundations of Machine Learning (statisticalhorizons.com, 2025-04-18). Explore the intersection of statistics and machine learning, highlighting tools like linear and logistic regression, Lasso regression, and Naive Bayes, showing how statistical foundations underpin modern predictive techniques

Off with the polynomial’s tail! (alexshtf.github.io, 2025-04-17). Explore overparametrized Legendre polynomial regression using Scikit-Learn, analyzing the double descent phenomenon and polynomial pruning to capture dataset patterns without higher-degree coefficients

S3, Archimedes, SU2, and the semicircle (djalil.chafai.net, 2025-04-18). Exploring connections between semicircle distribution, S3 sphere, SU2 matrices, and mathematical concepts like Archimedes theorem and Sato-Tate conjecture across quaternionic and algebraic domains

New Proof Settles Decades-Old Bet About Connected Networks (quantamagazine.org, 2025-04-18). Peter Sarnak and Noga Alon's decades-old bet on the prevalence of optimal expander graphs has been settled; both were proven wrong using advanced insights from random matrix theory and eigenvalue distributions

Fredholm index (johndcook.com, 2025-04-21). The Fredholm index measures the difference between kernel and cokernel dimensions of operators, illustrating connections to the Euler characteristic and the Fredholm alternative theorem

The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect (juliabloggers.com, 2025-04-20). Automatic differentiation in scientific machine learning can yield inaccurate gradients due to numerical instabilities, as demonstrated in ODE and PDE contexts using Jax and PyTorch libraries, with proposals for mitigation in Julia SciML libraries

When Physics Meets Finance: Using AI to Solve Black-Scholes (towardsdatascience.com, 2025-04-18). Explore how Physics-Informed Neural Networks (PINNs) can be applied to financial models like Black-Scholes, integrating differential equations with AI to enhance option pricing predictions

Differentiable Programming from Scratch (thenumb.at, 2025-04-17). Differentiable programming leverages derivatives for optimization in fields like machine learning and computer graphics, utilizing concepts such as gradients, Jacobians, directional derivatives, numerical differentiation, and automatic differentiation techniques

On the Tunability of Random Survival Forests Model for Predictive Maintenance (arxiv:stat, 2025-04-20). The Random Survival Forest model's hyperparameter tuning significantly improves performance in predictive maintenance, evidenced by increased C-index and decreased Brier score across datasets, highlighting specific parameter impacts and optimal tuning ranges

Can Moran Eigenvectors Improve Machine Learning of Spatial Data? Insights from Synthetic Data Validation (arxiv:stat, 2025-04-16). Moran Eigenvector Spatial Filtering's effectiveness in machine learning is evaluated, revealing that models using only location coordinates outperformed eigenvector-based approaches in synthetic datasets with spatially varying effects

Fine Flood Forecasts: Incorporating local data into global models through fine-tuning (arxiv:cs, 2025-04-17). Flood forecasting can be enhanced by pre-training machine learning models on global datasets and then fine-tuning them with local data, leading to significant performance improvements in underperforming watersheds

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study (arxiv:cs, 2025-04-16). This study benchmarks several continual learning strategies, including Replay, on engineering regression tasks, demonstrating improved performance and reduced training time while addressing catastrophic forgetting across five datasets

SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields (arxiv:cs, 2025-04-16). SCENT is a scalable spatiotemporal learning framework that integrates interpolation, reconstruction, and forecasting using a transformer-based model, learnable queries, cross-attention, and a sparse attention mechanism for robust performance across diverse scientific tasks