Paper Review
  • Paper Review
    • NLP
      • Latent Retrieval for Weakly Supervised Open Domain Question Answering
      • Dense Passage Retrieval for Open-Domain Question Answering
      • BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Gener, Trans, Comprehension
      • Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • Condenser: a Pre-training Architecture for Dense Retrieval
      • InPars: Data Augmentation for Information Retrieval using Large Language Models
      • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
      • LoRA: Low-Rank Adaptation of Large Language Models
      • LLaMA: Open and Efficient Foundation Language Models
      • TableLlama: Towards Open Large Generalist Models for Tables
    • CV
      • SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
      • ViT : An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
    • VLM
      • Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
      • CLIP: Learning Transferable Visual Models From Natural Language Supervision
      • ALBEF: Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
      • MVLM: Masked Vision and Language Modeling for Multi-modal Representation Learning
      • SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
      • VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
      • CoCa: Contrastive Captioners are Image-Text Foundation Models
      • Flamingo: a Visual Language Model for Few-Shot Learning
      • GIT: A Generative Image-to-text Transformer for Vision and Language
      • LLaVA: Large Language and Vision Assistant (Visual Instruction Tuning)
    • Medical Domain application
      • Radiology-Llama2: Best-in-Class Large Language Model for Radiology
      • Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
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