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        • Conjugate Priors
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  • Machine Learning
    • Clustering
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  • Deep Learning
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    • Loss function
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    • Softmax Classifier and Cross Entropy
    • Normalization
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    • Variational Inference
    • VAE: Variational Auto-Encoders
    • Generative vs Discriminative
      • Generative Modelling
    • Making GANs train
    • Dimensionality of Layer Vs Number of Layers
    • Deep learning techniques
    • Dilated Convolutions
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    • Off Policy vs On Policy
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    • Deep Reinforcement Learning
      • Value Function Approximation
      • Policy Gradient
        • Algorithms
    • Multi Agent Reinforcement Learning
    • Reinforcement Learning - Sutton and Barto
      • Chapter 3: Finite Markov Decision Processes
      • Chapter 4: Dynamic Programming
    • MBRL
  • Transformers
    • Tokenziation
    • Embedding
      • Word Embedding
      • Positional Encoding
    • Encoder
    • Decoder
    • Multi-head Attention Block
    • Time Complexities of Self-Attention
    • KV Cache
    • Multi-head Latent Attention
    • Speculative Decoding
    • Flash Attention
    • Metrics
  • LLMs
    • LLM Techniques
    • LLM Post-training
    • Inference/Test Time Scaling
    • Reasoning Models
    • Reward Hacking
  • Diffusion Models
    • ImageGen
  • Distributed Training
  • State Space Models
  • RLHF
  • Robotics
    • Kalman Filter
    • Unscented Kalman Filter
  • Game Theory and ML
    • 1st Lecture - 19/01
    • Lecture 2 - 22/01
    • Lecture 4: Optimization
  • Continual Learning
    • Lecture - 21/01
    • iCaRL: Incremental Classifier and Representation Learning
    • Variational Continual Learning
  • Computer Vision
    • Hough Transform
    • Projective Geometry
      • Extrinsic and Intrinsic Parameters
      • Image Rectification
    • Tracking
    • Optical Flow
    • Harris Corner
    • Others
  • Papers
    • To Be Read
    • Probabilistic Object Detection and Uncertainty Estimation
      • BayesOD
      • Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
      • Gaussian YOLOv3
      • Dropout Sampling for Robust Object Detection in Open-Set Condition
      • *Sampling Free Epistemic Uncertainty Estimation using Approximated Variance Propagation
      • Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
      • Can We Trust You? On Calibration of Probabilistic Object Detector for Autonomous Driving
    • Object Detection
    • Temporal Fusion in Object Detection/ Video Object Detection
    • An intriguing failing of convolutional neural networks and the CoordConv solution
    • A Neural Algorithm of Artistic Style - A.Gatys
  • Deep Learning Book
    • Chapter 4: Optimization
    • Chapter 5: Machine Learning Basics
    • Chapter 6: Deep FeedForward Networks
  • Python
    • Decorators
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      • Pip
    • Gotchas
    • Async functions
  • Computer Science
  • TensorFlow
  • Pytorch
    • RNN/LSTM in Pytorch
    • Dataset/ Data loader
    • Resuming/Loading Saved model
  • Programming
    • Unit Testing
    • How to write code
  • General Software Engineering
    • SSH tunneling and Ngrok
  • How To Do Research
  • Resources
  • ROS for python3
  • Kitti
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  1. Transformers

Flash Attention

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Last updated 8 months ago

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