mailitics

Tag: transformers

  • Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,lambda}$ Targets

    Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,lambda}$ Targets arXiv:2602.20555v1 Announce Type: new Abstract: The tremendous success of Transformer models in fields such as large language models and computer vision necessitates a rigorous theoretical investigation. To the best of our knowledge, this paper is the first work proving that standard Transformers can…

  • Hugging Face Transformers in Action: Learning How To Leverage AI for NLP

    Hugging Face Transformers in Action: Learning How To Leverage AI for NLP A practical guide to Hugging Face Transformers and to how you can analyze your resumé sentiment in seconds with AI The post Hugging Face Transformers in Action: Learning How To Leverage AI for NLP appeared first on Towards Data Science. Gustavo Santos Go…

  • The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel

    The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards…

  • When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation

    When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation Exploring the frequency fingerprints of Transformers to guide smarter knowledge distillation The post When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation appeared first on Towards Data Science. Ankit Singh Chauhan Go to original source

  • Scaling Recommender Transformers to a Billion Parameters

    Scaling Recommender Transformers to a Billion Parameters How to implement a new generation of transformer recommenders The post Scaling Recommender Transformers to a Billion Parameters appeared first on Towards Data Science. Kirill Кhrylchenko Go to original source

  • Transformers, Time Series, and the Myth of Permutation Invariance

    Transformers, Time Series, and the Myth of Permutation Invariance There’s a common misconception in ML/DL that Transformers shouldn’t be used for forecasting because attention is permutation-invariant. Latest evidence shows the opposite, such as Google’s latest model, where the experiments show the model performs just as well with or without positional embeddings. You can find an…

  • Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning

    Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning arXiv:2508.16027v1 Announce Type: new Abstract: Transformers have demonstrated exceptional performance across a wide range of domains. While their ability to perform reinforcement learning in-context has been established both theoretically and empirically, their behavior in non-stationary environments remains less understood. In this study, we address this gap…

  • LLMs are Bayesian, in Expectation, not in Realization

    LLMs are Bayesian, in Expectation, not in Realization arXiv:2507.11768v1 Announce Type: new Abstract: Large language models demonstrate remarkable in-context learning capabilities, adapting to new tasks without parameter updates. While this phenomenon has been successfully modeled as implicit Bayesian inference, recent empirical findings reveal a fundamental contradiction: transformers systematically violate the martingale property, a cornerstone requirement…

  • Audio Spectrogram Transformers Beyond the Lab

    Audio Spectrogram Transformers Beyond the Lab A recipe for building a portable soundscape monitoring app with AudioMoth, Raspberry Pi, and a decent dose of deep learning. The post Audio Spectrogram Transformers Beyond the Lab appeared first on Towards Data Science. Maciej Adamiak Go to original source

  • Behind the Magic: How Tensors Drive Transformers

    Behind the Magic: How Tensors Drive Transformers Introduction Transformers have changed the way artificial intelligence works, especially in understanding language and learning from data. At the core of these models are tensors (a generalized type of mathematical matrices that help process information) . As data moves through the different parts of a Transformer, these tensors…

  • Vision Transformers (ViT) Explained: Are They Better Than CNNs?

    Vision Transformers (ViT) Explained: Are They Better Than CNNs? 1. Introduction Ever since the introduction of the self-attention mechanism, Transformers have been the top choice when it comes to Natural Language Processing (NLP) tasks. Self-attention-based models are highly parallelizable and require substantially fewer parameters, making them much more computationally efficient, less prone to overfitting, and…

  • Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I

    Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I Master Fine-Tuning Transformers, Comparing Deep Learning Architectures, and Deploying Sentiment Analysis Models Continue reading on Towards Data Science » Leo Anello Go to original source

  • Transformers Key-Value (KV) Caching Explained

    Transformers Key-Value (KV) Caching Explained Speed up your LLM inference Continue reading on Towards Data Science » Michał Oleszak Go to original source