Data Science

Scientists uncover new method to generate protein datasets for training AI

Top 10 AI certifications and courses for 2026

This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...

What image/video training data is hardest to find right now? [R]

I'm building a crowdsourced photo collection platform (contributors take photos with smartphones, we auto-label with YOLO/CLIP + enrich w...

[2602.13649] Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series

The paper introduces the Joint Time Series Chain (JTSC) concept, enhancing the detection of unusual evolving trends across interrupted or...

[2602.13880] VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection

The paper presents VSAL, a vision-based framework for graph property detection that utilizes adaptive layouts to enhance the detection of...

[2602.13634] Optimization-Free Graph Embedding via Distributional Kernel for Community Detection

This article presents a novel method for graph embedding that addresses over-smoothing in Neighborhood Aggregation Strategy (NAS) methods...

[2602.13626] Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?

This paper examines benchmark data leakage in LLM-based recommendation systems, revealing how it can distort performance metrics and misl...

[2602.13852] Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking

The paper presents the Experimentation Accelerator, a framework that enhances A/B testing by providing interpretable insights and creativ...

[2602.13586] Interpretable clustering via optimal multiway-split decision trees

This paper presents a novel clustering method using optimal multiway-split decision trees, enhancing interpretability and accuracy while ...

[2602.13550] Out-of-Support Generalisation via Weight Space Sequence Modelling

This paper presents a novel approach to out-of-support generalization in machine learning, introducing the WeightCaster framework for imp...

[2602.13532] Fast Swap-Based Element Selection for Multiplication-Free Dimension Reduction

This paper presents a fast algorithm for element selection in dimension reduction, eliminating multiplication to enhance efficiency in re...

[2602.13531] QuaRK: A Quantum Reservoir Kernel for Time Series Learning

The paper introduces QuaRK, a novel quantum reservoir computing framework designed for efficient time series learning, emphasizing its em...

[2602.13769] OR-Agent: Bridging Evolutionary Search and Structured Research for Automated Algorithm Discovery

The paper presents OR-Agent, a multi-agent framework designed to automate scientific discovery through structured hypothesis management a...

[2602.13506] $γ$-weakly $θ$-up-concavity: Linearizable Non-Convex Optimization with Applications to DR-Submodular and OSS Functions

The paper introduces $γ$-weakly $θ$-up-concavity, a new condition for optimizing monotone non-convex functions, enhancing approximation g...

[2602.13697] No Need to Train Your RDB Foundation Model

The paper presents a novel approach to utilizing relational databases (RDBs) for predictive modeling without the need for retraining mode...

[2602.13485] Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability

This paper presents a federated learning framework for understanding nonlinear temporal dynamics across decentralized systems, enhancing ...

[2602.13482] Comparing Classifiers: A Case Study Using PyCM

This paper explores the PyCM library for evaluating multi-class classifiers, emphasizing the importance of diverse evaluation metrics in ...

[2602.13616] DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving

The paper introduces DiffusionRollout, a strategy for improving long-horizon predictions in physical systems governed by PDEs by addressi...

[2602.13416] High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator

This article presents a novel approach to high-resolution climate projections using a diffusion-based downscaling framework applied to a ...

[2602.13413] Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise

This article presents a worst-case complexity theory for stochastically preconditioned stochastic gradient descent (SPSGD) under heavy-ta...

[2602.13398] Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization

This article presents a novel framework for accelerating the discovery of cryoprotectant cocktails using multi-objective Bayesian optimiz...

[2602.13359] The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric

This article introduces the Speed-up Factor, a new performance metric for evaluating multi-iteration active learning methods, demonstrati...

[2602.13348] Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset

This article evaluates the performance of advanced machine learning architectures on the MNIST-1D dataset, demonstrating their effectiven...