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Tag: multiple

  • AI in Multiple GPUs: ZeRO & FSDP

    AI in Multiple GPUs: ZeRO & FSDP Learn how Zero Redundancy Optimizer works, how to implement it from scratch, and how to use it in PyTorch The post AI in Multiple GPUs: ZeRO & FSDP appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source

  • AI in Multiple GPUs: How GPUs Communicate

    AI in Multiple GPUs: How GPUs Communicate A deep dive into the hardware infrastructure that enables multi-GPU communication for AI workloads The post AI in Multiple GPUs: How GPUs Communicate appeared first on Towards Data Science. Lorenzo Cesconetto Go to original source

  • Blessings of Multiple Good Arms in Multi-Objective Linear Bandits

    Blessings of Multiple Good Arms in Multi-Objective Linear Bandits arXiv:2602.12901v1 Announce Type: new Abstract: The multi objective bandit setting has traditionally been regarded as more complex than the single objective case, as multiple objectives must be optimized simultaneously. In contrast to this prevailing view, we demonstrate that when multiple good arms exist for multiple objectives,…

  • The Cost of Learning under Multiple Change Points

    The Cost of Learning under Multiple Change Points arXiv:2602.11406v1 Announce Type: new Abstract: We consider an online learning problem in environments with multiple change points. In contrast to the single change point problem that is widely studied using classical “high confidence” detection schemes, the multiple change point environment presents new learning-theoretic and algorithmic challenges. Specifically,…

  • PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

    PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch Hands-on PyTorch: Building a 3-layer neural network for multiple regression The post PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch appeared first on Towards Data Science. Gustavo Santos Go to original source

  • Multiple Linear Regression Explained Simply (Part 1)

    Multiple Linear Regression Explained Simply (Part 1) The math behind fitting a plane instead of a line. The post Multiple Linear Regression Explained Simply (Part 1) appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions

    SADA: Safe and Adaptive Inference with Multiple Black-Box Predictions arXiv:2509.21707v1 Announce Type: new Abstract: Real-world applications often face scarce labeled data due to the high cost and time requirements of gold-standard experiments, whereas unlabeled data are typically abundant. With the growing adoption of machine learning techniques, it has become increasingly feasible to generate multiple predicted…

  • System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework

    System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework arXiv:2509.16663v1 Announce Type: new Abstract: ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically dependent. In reality,…

  • Class conditional conformal prediction for multiple inputs by p-value aggregation

    Class conditional conformal prediction for multiple inputs by p-value aggregation arXiv:2507.07150v1 Announce Type: new Abstract: Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an innovative refinement to these methods for classification tasks, specifically tailored for scenarios where multiple observations (multi-inputs) of a…

  • Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking

    Attention-Bayesian Hybrid Approach to Modular Multiple Particle Tracking arXiv:2506.09441v1 Announce Type: new Abstract: Tracking multiple particles in noisy and cluttered scenes remains challenging due to a combinatorial explosion of trajectory hypotheses, which scales super-exponentially with the number of particles and frames. The transformer architecture has shown a significant improvement in robustness against this high combinatorial…

  • Multiple Linear Regression Analysis

    Multiple Linear Regression Analysis Implementation of multiple linear regression on real data: Assumption checks, model evaluation, and interpretation of results using Python. The post Multiple Linear Regression Analysis appeared first on Towards Data Science. JUNIOR JUMBONG Go to original source

  • Multiple Linked Tensor Factorization

    Multiple Linked Tensor Factorization arXiv:2502.20286v1 Announce Type: new Abstract: In biomedical research and other fields, it is now common to generate high content data that are both multi-source and multi-way. Multi-source data are collected from different high-throughput technologies while multi-way data are collected over multiple dimensions, yielding multiple tensor arrays. Integrative analysis of these data…

  • Deep Dive into Multithreading, Multiprocessing, and Asyncio

    Deep Dive into Multithreading, Multiprocessing, and Asyncio How to choose the right concurrency model Image by Paul Esch-Laurent from Unsplash Python provides three main approaches to handle multiple tasks simultaneously: multithreading, multiprocessing, and asyncio. Choosing the right model is crucial for maximising your program’s performance and efficiently using system resources. (P.S. It is also a common interview…