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Machine Learning Notes

SnapKV[WIP]

Machine Learning Notes

ML Notes
Fundamentals
Fundamentals
Weight Initializers
Optimizers
Optimizers
- Adam
- SGD
Regularization
Convolution
Max Pooling
Batch Norm
RNN
LSTM
Tokenization
Tokenization
Models
Models
- RCNN
- Faster RCNN
- FCOS
- VGG
Papers
Papers
- Vision
  Vision
- Fine Tuning
  Fine Tuning
  - LoRA
- NLP
  NLP
  - Seq2Seq
  - Additive Attention
  - Dot Product Attention
  - Transformer
  - GPT-1
  - BERT
  - T5
  - GPT-2
  - LLaMA
  - Efficient Transformers
  - SnapKV[WIP] SnapKV[WIP]
    Table of contents
    
    Introduction
    
    Observations
    
    Methodology
    
    Experiments & Results
  - R-KV Cache[WIP]
  - S4-Structured State Spaces
  - Mamba

Table of contents

Introduction
Observations
Methodology
Experiments & Results

SnapKV: LLM Knows What You are Looking for Before Generation

Introduction

Observations

Methodology

Experiments & Results

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