Machine Learning Engineer: 29th April 2025

Numerical stability showcase: Ranking with SoftMax or Boltzmann factor (memosisland.blogspot.com, 2025-04-22). Examines numerical stability in ranking using SoftMax and the Boltzmann factor, highlighting examples of instability and solutions like LogSoftMax in probabilistic interpretations of scores

Cracking the code: How neural networks might actually “think” (developers.redhat.com, 2025-04-23). A combinatorial approach is introduced to understand neural networks' internal computations, enhancing mechanistic interpretability and offering insights into their logic via concepts like feature channel coding and Boolean expressions

The urgency of interpretability (darioamodei.com, 2025-04-28). Dario Amodei discusses the importance of interpretability in AI, emphasizing tools like mechanistic interpretability, circuits, and sparse autoencoders, advocating for transparency to mitigate risks and enhance understanding of AI models

Rise of the machine cosmologists (firstprinciples.org, 2025-04-24). AI in cosmology is advancing through tools like SimBIG at Harvard and Flatiron Institute, aiding in the study of dark matter and enhancing cosmic simulations with higher precision and insights into the universe's structure

Improving Deep Learning With a Little Help From Physics (quantamagazine.org, 2025-04-23). Rose Yu combines physics principles with deep learning to enhance AI applications, improving traffic predictions, simulating turbulence, and aiding COVID-19 spread forecasts through innovative neural networks and graph theory

CosAE: Learnable Fourier Series for Image Restoration (sifeiliu.net, 2025-04-26). CosAE utilizes learnable Fourier series via a feed-forward neural network to achieve extreme spatial compression for image restoration, outperforming state-of-the-art methods in super-resolution and blind image restoration tasks

SQL Schema for Durable Event Processing (rnowling.github.io, 2025-04-23). RJ Nowling discusses using PostgreSQL to build a durable event processing system, replacing Apache Kafka and MongoDB to simplify event-streaming, model training, and online evaluation in his ML Production Systems class

Weeknotes: 28th April 2025 (digitalflapjack.com, 2025-04-28). Michael Winston Dales explores data mining in LIFE runs, highlights floating point issues, evaluates performance of the Yirgacheffe library with OCaml, and discusses his experience with Python and parallelism in programming

Towards the cutest neural network and dubious ideas for a code CAD language (kevinlynagh.com, 2025-04-27). Kevin explores using a simple neural network for sensor pose estimation and designs a code CAD language with multiple dispatch, discussing challenges faced with quantization and user-friendly textual notation

Gemini Flash Pretraining (vladfeinberg.com, 2025-04-24). A literature review covering scaling laws in machine learning and Gemini Flash Pretraining, featuring insights from industry and references to significant works, including external presentations by Sebastian Borgeaud and Jean-Baptiste Alayrac

proximal sampler (xianblog.wordpress.com, 2025-04-27). Andre Wibisono presented on the proximal sampler targeting demarginalised densities, demonstrating its convergence properties compared to Metropolis algorithms under log-concavity assumptions during a Columbia workshop

Geometric Deep Learning: AI Beyond Text & Images (medium.datadriveninvestor.com, 2025-04-24). Geometric Deep Learning (GDL) extends traditional AI techniques to handle non-Euclidean data, utilizing graph neural networks and enabling applications in drug discovery, medical imaging, and anomaly detection across various complex structures

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch (arxiv.org, 2025-04-24). PyGraph introduces robust compiler support for CUDA graphs in PyTorch, enhancing performance and usability in machine learning tasks while leveraging advanced computational capabilities

Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment (arxiv:cs, 2025-04-25). Tree boosting methods like XGBoost are evaluated for imbalanced classification in risk assessment, showcasing performance improvement with data volume and careful hyper-parameter optimization, but diminishing returns from sampling on data distribution

Contextures: The Mechanism of Representation Learning (arxiv:stat, 2025-04-28). Contexture theory characterizes representation learning via associations between input and context. It introduces objectives like SVME and KISE, proving statistical learning bounds and highlighting diminishing returns from merely increasing model size

Label-independent hyperparameter-free self-supervised single-view deep subspace clustering (arxiv:cs, 2025-04-25). A novel single-view deep subspace clustering method using layer-wise self-expression loss, subspace-structured norms, and a relative error-based self-stopping mechanism is introduced, enhancing clustering quality without requiring hyperparameter tuning or labels

High-performance training and inference for deep equivariant interatomic potentials (arxiv:cs, 2025-04-22). The NequIP framework overhaul enhances multi-node parallelism and performance for deep equivariant neural networks in molecular dynamics, utilizing the PyTorch 2.0 compiler and introducing a custom tensor product kernel, achieving up to 18x acceleration

PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology (arxiv:cs, 2025-04-25). PHeatPruner integrates persistent homology and sheaf theory for interpretable feature selection in multivariate time series classification, reducing variable count by 45% while preserving model accuracy across several machine learning algorithms

MLOps Monitoring at Scale for Digital Platforms (arxiv:stat, 2025-04-23). A new monitoring framework, the Machine Learning Monitoring Agent (MLMA), enables scalable ML model monitoring with automated re-training in unstable streaming data environments, validated in a large-scale last-mile delivery platform test