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Transformers

  • An optimization loss landscape
    Quantum Algorithms, Quantum Machine Learning

    New Ranking Loss Boosts Quantum Architecture Search Performance

    by The NeuronJuly 30, 2026
  • Transformer Model Achieves Native Multimodal Support for Video, Audio
    Artificial Intelligence, Machine Learning

    Transformer Model Achieves Native Multimodal Support for Video, Audio

    by The NeuronMay 20, 2026
  • Trail of glowing blue and green circular energy orbs receding into the distance on a dark background
    Artificial Intelligence

    Longer AI Contexts Weaken Privacy and Accuracy

    by Muhammad Rohail T.February 18, 2026
  • Ai’s ‘attention’ System Understood, Paving the Way for Limitless Context Processing
    Artificial Intelligence

    AI Attention System Enables Limitless Context Processing

    by Muhammad Rohail T.February 13, 2026
  • Neural Networks Demonstrate Bayesian Uncertainty Tracking Via Implicit EM with Distances
    Artificial Intelligence

    Neural Networks Track Bayesian Uncertainty with Distances

    by Muhammad Rohail T.January 7, 2026
  • Spiking Neuromorphic Transformer Achieves Attention Via Synaptic Plasticity, Reducing Energy Costs Beyond 0.49
    Science

    Neuromorphic Transformer: Synaptic Plasticity & Low Energy

    by Muhammad Rohail T.November 20, 2025
  • Researchers Achieve 17-fold Speed-up in Materials Science with Universal MLIPs and 6% Accuracy
    Artificial Intelligence

    MLIPs Speed Materials Science by 17x with 6% Accuracy

    by Dr. DonovanSeptember 1, 2025
  • HyDRA: AI Improves Wireless Device Recognition
    Artificial Intelligence

    HyDRA: AI Improves Wireless Device Recognition

    by Dr. DonovanJuly 17, 2025
  • MambaNeXt-YOLO: Efficient Real-time Object Detection with State Space Models.
    Artificial Intelligence

    MambaNeXt-YOLO: Real-time Object Detection with SSMs

    by Dr. DonovanJune 6, 2025
  • Transformers: A Novel Framework for Efficient Uncertainty Quantification Using In-Context Learning & Conformal Prediction
    Artificial Intelligence

    Transformers: Uncertainty Quantification with In-Context

    by Dr. DonovanApril 24, 2025
  • The Rise of Generative AI: How Machines Learned to Create
    Artificial Intelligence

    The Rise of Generative AI: How Machines Learned to Create

    by Dr. DonovanApril 21, 2025
  • Dynamic Tanh Beats Normalization in Transformers
    Artificial Intelligence, Machine Learning

    Dynamic Tanh Beats Normalization in Transformers

    by Dr. DonovanMarch 18, 2025
  • Design Knowledge Boosts Accuracy in Large Language Models
    Artificial Intelligence

    Design Knowledge Boosts Accuracy in Large Language Models

    by Dr. DonovanNovember 26, 2024
  • Revolutionising Language Models: MatMul-Free Method Achieves High Performance with 61% Less Memory Usage
    Artificial Intelligence

    Language Models: Memory-Efficient MatMul Method

    by Dr. DonovanJune 9, 2024
  • Japan Unveils Fugaku-LLM: Supercomputer-Trained Language Model Revolutionising AI Research and Business
    Artificial Intelligence

    Fugaku-LLM: Japan’s Supercomputer Language Model

    by Dr. DonovanMay 15, 2024
  • Large-scale machine-learning methods have shown a surprising ability to forecast chaotic systems beyond typical predictability horizons. These methods, such as transformers or recurrent neural networks, outperform specialised methods grounded in dynamical systems theory, like reservoir computers or neural ordinary differential equations, especially when there is a lot of data available. However, in data-limited settings, physics-based hybrid methods retain an advantage due to their strong inductive biases. The study, conducted by William Gilpin (The University of Texas at Austin, Austin, Texas) also found that the Lyapunov exponent, a measure of chaos, does not correlate with the accuracy of different forecasting methods.
    Artificial Intelligence, Physics, Technology News

    Machine Learning Forecasts Chaos Better Than Physics

    by Physics NewsDecember 22, 2023

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