🪴 My Second Brain

Search

SearchSearch
            • Beyond neural scaling laws beating power law scaling via data pruning
            • Deep Learning on a Data Diet Finding Important Examples Early in Training
            • SemDeDup Data-efficient learning at web-scale through semantic deduplication
              • LSH-MinHash
              • Memorization Without Overfitting Analyzing the Training Dynamics of Large Language Models
              • When Less is More Investigating Data Pruning for Pretraining LLMs at Scale
              • Language Models are Few-Shot Learners
              • The pile An 800gb dataset of diverse text for language modeling
            • D4 Improving LLM Pretraining via Document De-Duplication and Diversification
            • Scaling Laws for Neural Language Models
            • Textbooks are All You Need
          • Embedding
          • Inference
          • Language Models are Few-Shot Learners
          • Learning
          • Tokenization
          • 메모
          • 타임라인
            • Deep contextualized word representations
            • Explaining How Transformers Use Context to Build Predictions
            • Rethinking the Inception Architecture for Computer Vision
            • 작성 요령
      • Table
      • tools
      • Words
      • 사전 학습용 텍스트 데이터셋 평가
    Home

    ❯

    논문 정리

    ❯

    Language Model

    ❯

    Learning

    Learning

    Mar 13, 2024, 1 min read

    Exploring the Limits of Transfer Learning with a Unified Text-to-Text

    Raffel, Colin, et al. “Exploring the limits of transfer learning with a unified text-to-text transformer.” The Journal of Machine Learning Research 21.1 (2020): 5485-5551. https://blog.research.google/2020/02/exploring-transfer-learning-with-t5.html

    Graph View

    Backlinks

    • No backlinks found

    Created with Quartz v4.2.2 © 2024

    • Github
    • Tistory