Data analysis, statistics, and data engineering
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Google's new offline-first dictation app uses Gemma AI models to take on the apps like Wispr Flow.
This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...
InScope, an AI-powered financial reporting startup founded by former accountants, raises $14.5 million to automate the tedious process of...
A Reddit user seeks remote machine learning gigs and research opportunities, highlighting the growing demand for ML professionals in the ...
Discussion thread for the upcoming release of FAccT 2026 paper reviews, encouraging community engagement and insights on fairness, accoun...
Presearch's new tool, Doppelgänger, allows users to find OnlyFans models resembling celebrities, aiming to improve content discovery whil...
This article presents a hybrid architecture combining Multi-Agent Reinforcement Learning (MARL) and Linear Programming (LP) for optimizin...
The article discusses how AI is transforming geotechnical engineering by automating tasks, enhancing data analysis, and creating new skil...
The article discusses how to fine-tune an ASR model for multilingual IPA transcription, seeking advice on model selection and training st...
![[2601.16174] Beyond Predictive Uncertainty: Reliable Representation Learning with Structural Constraints](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper introduces a framework for reliable representation learning in machine learning, emphasizing the importance of representation-...
The paper introduces Ringleader ASGD, an asynchronous SGD algorithm that achieves optimal time complexity under data heterogeneity, addre...
This article reviews and compares nonlinear model order reduction methods for dynamical systems in process engineering, highlighting thei...
This paper presents a new framework, MAR-S, for robust and efficient inference with unstructured data, addressing biases in neural networ...
This article presents a novel method for inferring entropy production in many-body systems using a nonequilibrium maximum entropy approac...
This article presents a model-based data selection framework for enhancing multilingual LLM pretraining, demonstrating significant effici...
This article examines the finite-sample performance of the maximum likelihood estimator (MLE) in logistic regression, focusing on its exi...
This article explores how input-label correlation influences the performance of random feature models (RFMs) in machine learning, particu...
This article presents a novel parameter-free Adaptive Resonance Theory-based topological clustering algorithm that enhances clustering pe...
The paper presents a universal diffusion-based framework for downscaling weather forecasts, enhancing low-resolution predictions into hig...
This paper explores active learning for decision trees, presenting a new algorithm that achieves polylogarithmic label complexity with pr...
This paper presents a unified perspective on learning PDE solvers, integrating Physics-Informed Neural Networks and Neural Operators to e...
This article presents a novel North-East monsoon climate index for improving rainfall predictions in Thailand, utilizing reinforcement le...
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