Planetary prediction engine: Automating global models via Earth AI
Earth AI
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AI Research
The Sigmoid Function: From 'e' to Neural Networks
We use the equation all the time. But where did it actually come from? The post The Sigmoid Function: From 'e' to Neural Networks appeared first on Towards Data Science.
I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
What a final-year project taught me about the gap between evaluation metrics and production decisions The post I Trained Six Models for Fraud Detection, and the Best One Isn't in Production appeared first on Towards…
Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch
As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter? The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch…
How to Work with AI Coding Agents
A practical guide to getting better code, not just more code The post How to Work with AI Coding Agents appeared first on Towards Data Science.
Stop Giving Your AI Agent a Search Box and Start Giving It Typed Tools, Hard Bounds, and a Gate It Cannot Talk Past
What happens when you stop feeding a model context and let it go find its own, walking a knowledge graph within strict limits, and what four models and one wrong prediction revealed about whether that is worth doing.…
Privileged Likelihood Is Not Automatically Value: Three Checks for Token Credit in On-Policy Self-Distillation
arXiv:2608.09263v2 Announce Type: replace-cross Abstract: On-policy self-distillation aims to improve upon reinforcement learning from verifiable rewards (RLVR) by providing token-level scores derived from privileged…
Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon
arXiv:2608.25990v1 Announce Type: new Abstract: Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletely understood. We…
Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence
arXiv:2607.29456v2 Announce Type: replace-cross Abstract: Double Machine Learning (DML) is a popular approach for treatment effect estimation in various settings, which allows a wide range of flexible machine learning…
Learning Continuous Regional Temperature Fields with Lead-Time and Resolution Queries
arXiv:2608.25823v1 Announce Type: new Abstract: Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep learning-based regional…
Activation Steering Transfer to Agents: One Gain Ratio Does Not Identify Potency and Efficacy
arXiv:2607.09156v2 Announce Type: replace Abstract: Additive activation steering is calibrated in single-turn chat and then deployed inside agent scaffolds. The quantity usually reported for that move is a gain: a ratio…
BRIDLE: Generalized Self-supervised Learning with Quantization
arXiv:2502.02118v2 Announce Type: replace Abstract: Self-supervised learning has been a powerful approach for learning meaningful representations from unlabeled data across various domains, reducing the reliance on…
StablePDENet: Enhancing Neural Operator Stability through Physics-Informed Residual-Sensitivity Regularization
arXiv:2601.06472v2 Announce Type: replace Abstract: Learning solution operators for differential equations with neural networks has shown great potential in scientific computing, but ensuring their stability under input…
The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline
arXiv:2608.24952v1 Announce Type: cross Abstract: Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language modeling pipeline…
Sample Margin-Aware Recalibration of Temperature Scaling
arXiv:2506.23492v2 Announce Type: replace Abstract: Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overconfident, posing risks for…
PA-CoT: Profile-Adaptive Chain-of-Thought for Personalized Nutritional Consulting
arXiv:2608.24907v1 Announce Type: cross Abstract: In health and nutrition consulting, widely used prompting methods pass the user profile as an unstructured block without a dedicated analysis step, leaving…
AI/ML Life Cycle Management for Interoperable AI Native RAN
arXiv:2507.18538v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling beam management, channel…
Forecasting Multiple Observables with SCROLL: Score-Trained Uncertainty for Stochastic Dynamics
arXiv:2608.25898v1 Announce Type: new Abstract: Forecasting a stochastic dynamical system rarely means a single number: one wants several observables---future state, threshold event, regime label---each with its own…
Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials
arXiv:2608.25448v1 Announce Type: cross Abstract: To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with…
MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
arXiv:2608.26094v1 Announce Type: cross Abstract: Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics such as muscle…
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