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Tag: low-rank adaptation

  • AI Knowledge Can Now Be Reliably Erased Even with Data Compression Techniques
    Artificial Intelligence

    AI Knowledge Erasure With Data Compression

    by Muhammad Rohail T.February 17, 2026
  • Achieves Photorealistic Image Super-Resolution with Enhanced Flow Map Diffusion Models
    Artificial Intelligence

    FlowMapSR: Photorealistic Image Super-Resolution

    by Muhammad Rohail T.January 28, 2026
  • Primal Achieves Faster Llama-13b Inference with LoRA Rank 8 on PIM
    Technology News

    PRIMAL Speeds Llama-13b Inference with LoRA & PIM

    by Muhammad Rohail T.January 23, 2026
  • Owlv2 Vision-Language Model Advances Crater Detection to 94.0% Accuracy
    Space

    Lunar Crater Detection: 94% Accuracy with Owlv2

    by Muhammad Rohail T.January 14, 2026
  • Llm-powered FasterPy Enables Faster Code, Addressing Costly Performance Issues
    Artificial Intelligence

    LLM Speeds Code with FasterPy

    by Muhammad Rohail T.January 6, 2026
  • Think-at-hard Selectively Iterates on 5.4% of Tokens, Improving Reasoning in Language Models with 12.6% Gains
    Artificial Intelligence

    LLM Reasoning Boosted with Selective Token Iteration

    by Muhammad Rohail T.November 15, 2025
  • Seqr: Unsupervised LoRA Routing Achieves Efficient, Secure Adapter Selection Via Activation Norm Maximization
    Quantum Features, Quantum Research News

    SEQR: Efficient Adapter Selection with LoRA Routing

    by Muhammad Rohail T.September 25, 2025
  • Adaptive Normalizing Flow Achieves 138.0-fold Speedup in Lattice Field Theory Simulations with Resistive Memory-based Neural Differential Equations
    Technology

    Lattice QFT Simulations Speedup with AI & Resistive Memory

    by Dr. DonovanSeptember 17, 2025
  • Phlora Extracts Data-free Low-Rank Adapters from Full-rank Checkpoints, Enabling Scalable and Cost-effective Model Serving
    Artificial Intelligence, Technology News

    Phlora Adapters: Scalable, Cost-Effective AI Serving

    by Dr. DonovanSeptember 17, 2025
  • Researchers Boost LLM Fine-tuning with 601 Parameters and QR Decomposition for 77x Gains
    Artificial Intelligence

    LLM Fine-tuning with 601 Parameters & QR Decomposition

    by Dr. DonovanSeptember 2, 2025
  • Efficient Continual Learning with TreeLoRA for Large Pre-trained Models.
    Artificial Intelligence

    TreeLoRA: Efficient Continual Learning for LLMs

    by Dr. DonovanJune 16, 2025
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