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논문 정리
Computer Vision
Dataset
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
Language Model
Dataset
Deduplicaion
LSH-MinHash
Memorization Without Overfitting Analyzing the Training Dynamics of Large Language Models
Pruning
When Less is More Investigating Data Pruning for Pretraining LLMs at Scale
Quality
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
작업실
번역 중
AI
Deep contextualized word representations
Explaining How Transformers Use Context to Build Predictions
Rethinking the Inception Architecture for Computer Vision
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tools
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Language Model
Folder: 논문-정리/Language-Model
15 items under this folder.
Mar 13, 2024
Scaling Laws for Neural Language Models
Mar 13, 2024
Textbooks are All You Need
Mar 13, 2024
Embedding
Mar 13, 2024
Inference
Mar 13, 2024
Language Models are Few-Shot Learners
Mar 13, 2024
Learning
Mar 13, 2024
Tokenization
Mar 13, 2024
메모
Mar 13, 2024
타임라인
#NLP
#CV
Mar 13, 2024
D4 Improving LLM Pretraining via Document De-Duplication and Diversification
Mar 13, 2024
LSH-MinHash
Mar 13, 2024
Memorization Without Overfitting Analyzing the Training Dynamics of Large Language Models
Mar 13, 2024
When Less is More Investigating Data Pruning for Pretraining LLMs at Scale
Mar 13, 2024
Language Models are Few-Shot Learners
Mar 13, 2024
The pile An 800gb dataset of diverse text for language modeling