Spotting the Unexpected: AI Learns to Detect Rare Driving Risks
A new approach uses unsupervised learning to identify unusual driving patterns that could signal potential safety hazards.
Science
Beyond the Lab: Building Machine Learning You Can Rely On
As machine learning models move into real-world applications, their performance can degrade when faced with unexpected data-this review explores how to ensure consistent reliability.
Predicting the Ocean’s Pulse with Deep Learning
A new study showcases how artificial intelligence can accurately forecast ocean dynamics using limited data from satellite observations.
Beyond Backtesting: Building Trustworthy Crypto Trading Strategies
![AutoQuant navigates the treacherous landscape of model optimization with a two-stage Bayesian search and double-screening process, ensuring stability through continuous live monitoring, and enforces rigorous financial alignment via a [latex]t\!+\!1[/latex] execution schedule and strict avoidance of predictive funding-a system designed to mitigate risk by prioritizing present certainty over speculative gain.](https://arxiv.org/html/2512.22476v1/figure0_flow.png)
New research highlights the critical need for realistic cost modeling and rigorous validation to prevent inflated performance estimates in cryptocurrency perpetual futures trading.
Seeing Through the Noise: AI-Powered Insights from Network Traffic
A new framework combines the power of large language models with targeted data retrieval to dramatically improve network traffic analysis and threat detection.
Chasing Signals in Shifting Markets
A new framework dynamically adjusts model complexity to improve financial forecasting and portfolio performance in the face of ever-changing market conditions.
Unlocking Transformer Secrets: A Deep Dive into Backpropagation
This review meticulously unpacks the backpropagation process within transformer networks, offering a clear pathway to understanding and optimizing these powerful architectures.
Beyond the Benchmark: Rethinking Deep Learning for Time Series
A critical look at how we evaluate deep learning models reveals that inconsistent practices are obscuring real progress in time series forecasting.
Predicting Gold’s Next Move: A Deep Learning Approach
![The algorithm anticipates market fluctuations through a layered predictive process-initial data collection feeds into preprocessing, followed by daily and monthly forecasting modeled with Long Short-Term Memory networks [latex]LSTM[/latex], then a Multi-Layer Perceptron [latex]MLP[/latex] fuses these forecasts, ultimately generating a dynamic trading strategy poised to respond to predicted shifts.](https://arxiv.org/html/2512.22606v1/gold_algo_steps.png)
Researchers have developed a novel hybrid neural network, optimized by a bio-inspired algorithm, to forecast gold prices with promising results.
Pricing for Platforms: From Confusion to Optimal Fees
New research reveals how third-party platforms can dynamically adjust service fees to maximize learning and minimize regret in the face of uncertain demand.