šŸŒ»
Models
  • Step by step intro
  • Bash
  • Git
    • Remove folder
  • Embedding
    • Normalize Input
    • One-hot
  • Hyperparameter tuning
    • Test vs Validation
    • Bias vs Variance
    • Input
      • Normalize input
      • Initialize weight
    • Hidden Layer
      • Hidden layer size
    • Learning Rate
      • Oscillate learning rate
      • Learning rate finder
    • Batch Size
    • Epoch
    • Gradient
      • Vanishing / Exploding Gradients
      • Gradient Checking
    • Cost Function
      • Loss Binary Cross Entropy
    • Regularization
      • Lā‚‚ regularization
      • Lā‚ regularization
      • Dropout regularization
      • Data augmentation
      • Early stopping
  • Fine-tuning
    • Re-train on new data
    • Freeze layer/weight
  • Common Graphing Stats
    • Confidence interval (CI) and error bar
    • Confusion matrix and type I type II error
    • Effect size
  • Models
    • Inverted Pendulum Model
    • Recurrent Neural Networks
      • GRU and LSTM
      • Neural Turing Machines
    • Hopfield
    • Attention
      • Re-attention
      • Enformer
    • Differential Equations
      • Ordinary Differential Equations
        • Language Ordinary Differential Equations (ODE)
        • Neural Ordinary Differential Equations (ODE)
          • Adjoint Sensitive Method
          • Continuous Backpropagation
          • Adjoint ODE
      • Partial Differential Equations
      • Stochastic Differential Equations
    • Knowledge Tracing Models
      • Bayesian Knowledge Tracing
    • trRosetta
    • Curve up grades
  • deeplearning.ai
    • Neural Networks and Deep Learning
      • Wk2 - Python Basics with Numpy
      • Wk2 - Logistic Regression with a Neural Network mindset
      • Wk3 - Planar data classification with a hidden layer
      • Wk4 - Building your Deep Neural Network: Step by Step
      • Wk4 - Deep Neural Network - Application
    • Hyperparameter Tuning, Regularization and Optimization
      • Wk1 - Initialization
      • Wk1 - Regularization
      • Wk1 - Gradient Checking
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
    • Sequence Models
  • Neuroscience Paper
    • Rotation and Head Direction
    • Computational Models of Memory Search
    • Bayesian Delta-Rule Model Explains the Dynamics of Belief Updating
    • Sensory uncertainty and spatial decisions
    • A Neural Implementation of the Kalman Filter
    • Place cells, spatial maps and the population code for memory (Hopfield)
    • Spatial Cognitive Map
    • Event Perception and Memory
    • Interplay of Hippocampus and Prefrontal Cortex in Memory
    • The Molecular and Systems Biology of Memory
    • Reconsidering the Evidence for Learning in Single Cells
    • Single Cortical Neurons as Deep Artificial Neural Networks
    • Magnetic resonance-based eye tracking using deep neural networks
Powered by GitBook
On this page

Was this helpful?

  1. Models
  2. Attention

Re-attention

PreviousAttentionNextEnformer

Last updated 3 years ago

Was this helpful?

To handle Attention Collapse or learning plateau: re-attention

Attention(Q,K,V)=softmax(Qā‹…KTd)ā‹…VAttention(Q, K, V) = softmax(\frac{{Q}\cdot{K}^{T}}{\sqrt{d}})\cdot{V}Attention(Q,K,V)=softmax(dā€‹Qā‹…KTā€‹)ā‹…V
Reāˆ’attention(Q,K,V)=Norm(ĪøT(softmax(Qā‹…KTd)))ā‹…VRe-attention(Q, K, V) = Norm(\theta^{T}(softmax(\frac{{Q}\cdot{K}^{T}}{\sqrt{d}})))\cdot{V}Reāˆ’attention(Q,K,V)=Norm(ĪøT(softmax(dā€‹Qā‹…KTā€‹)))ā‹…V

paper

Difference