A developer has built a depression detection engine using Python and OpenSMILE, extracting acoustic biomarkers from speech to identify indicators of depression. The system leverages the eGeMAPS featur…
A study using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development (ABCD) Study found that longitudinal models outperform baseline models in predicting adolescent …
XGBoost, an open-source library implementing gradient-boosted decision trees with a C++ core and CPU/CUDA/HIP backends, is examined in a technical deep dive covering its mathematical foundations and s…
Stripe, LLC. is hiring a Machine Learning Engineer in South San Francisco, CA, with a salary range of $212,000–$318,000 per year, to design and deploy ML models for underwriting and portfolio manageme…
Researchers introduced cUPMI, a class-conditional Gaussian augmentation for stacking ensembles in multimodal IPMN risk stratification, finding it consistently regularizes higher-capacity tree combiner…
Researchers at an unnamed institution developed a machine-learning pipeline to detect self-introductions in legislative committee testimony, using a dataset of 1.54 million utterances from five state …
A fraud detection model that predicts "not fraud" for every transaction scores 98.3% accuracy but is useless, according to a data scientist's account of building a payment-fraud ML pipeline. In the pi…
A developer tested major AI models including GPT-5.5, Gemini 2.0, Claude 3.5 Sonnet, and DeepSeek R1 on a Nifty options trading prediction task, finding that DeepSeek R1 achieved the highest accuracy …
A developer built an agent evaluation harness for local AI agents used in trading, addressing common pitfalls in agent testing such as lack of variation, failure modes, baselines, continuous testing, …
Prior Labs' TabPFN, a pre-trained Transformer for tabular data, enables zero-shot machine learning predictions without traditional training, feature engineering, or hyperparameter tuning, according to…
Prior Labs' TabPFN, a pre-trained Transformer for tabular data, enables zero-shot machine learning predictions without manual feature engineering or hyperparameter tuning. The model integrates with Sc…
ASOS, the online fashion retailer, is hiring a Machine Learning Engineer for its Search & Recommendations team in London, UK, with a hybrid work model. The role involves building and maintaining produ…
Kubernetes exists because running one container is easy, but operating many containers across many machines is not, according to a practical guide for MLOps engineers. The article traces the evolution…
A developer outlines a practical, phase-by-phase roadmap for becoming an AI/ML engineer, emphasizing hands-on projects and real-world deployment over tutorial-based learning. The guide covers fundamen…
Tabular foundation models (TFMs) such as TabPFN from PriorLabs and TabFM from Google are now outperforming gradient-boosted trees like XGBoost, CatBoost, and LightGBM on the TabArena benchmark, challe…
A new arXiv preprint (2607.27350v1) reports that XGBoost outperforms Transformer-based sequence models for Sybil bot detection on Ethereum under leakage-aware evaluation, while also providing lower la…
A guide from an unnamed data science source lists seven essential machine learning algorithms that remain relevant despite the rise of large language models and generative AI, arguing that simpler mod…
A 2026 study (arXiv:2606.00060) using XGBoost on 70,000 hours of BTC/USDT data reports ~65% annualised return with Sharpe above 1 after applying a cost-aware execution filter, but the edge does not su…
RelativeDB, an open source query engine built around the Relational Transformer architecture from Stanford's Tabular and Relational (STAR) Project, can outperform XGBoost on some tasks with minimal la…
Promograph uses Graph Neural Networks (GNNs) to model retail promotion waste by representing SKUs as nodes and co-purchase patterns as edges, enabling the propagation of discount impacts across the pr…