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Quantized Transfer Learning for Computer Vision Tutorial
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        • On Good Practices for Task Specific Distillation of Large Pretrained Visual Models
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        • On the Symmetries of Deep Learning Models and their Internal Representations
        • OpenELM An Efficient Language Model Family with Open source Training and Inference Framework
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        • Optimization Dynamics of Equivariant and Augmented Neural Networks
        • Parameter Efficient Fine tuning of Self supervised ViTs without Catastrophic Forgetting
        • Parameter Efficient Fine Tuning for Pre Trained Vision Models A Survey
        • Progress measures for grokking via mechanistic interpretability
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        • Refusal in Language Models Is Mediated by a Single Direction
        • Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems
        • Relaxing Equivariance Constraints with Non stationary Continuous Filters
        • Retrospective EIE Efficient Inference Engine onSparse and Compressed Neural Network
        • Revealing the Utilized Rank of Subspaces of Learning in Neural Networks
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        • SAM CLIP Merging Vision Foundation Models towards Semantic and Spatial Understanding
        • Scaling (Down) CLIP A Comprehensive Analysis of Data, Architecture, and Training Strategies
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        • Self Supervised Detection of Perfect and Partial Input Dependent Symmetries
        • SimPLR A Simple and Plain Transformer for Scaling Efficient Object Detection and Segmentation
        • Simultaneous linear connectivity of neural networks modulo permutation
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        • Surgical DINO Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery
        • Symmetries in Overparametrized Neural Networks A Mean Field View
        • Talaria Interactively Optimizing Machine Learning Models for Efficient Inference
        • The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof
        • The Lie derivative for measuring learned equivariance
        • The Unreasonable Ineffectiveness of the Deeper Layers
        • TiC CLIP Continual Training of CLIP models
        • Training quantized nets A deeper understanding
        • Understanding Deep Learning Chapter 10
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        • Using Degeneracy in the Loss Landscape for Mechanistic Interpretability
        • ViDT An Efficient and Effective Fully Transformer based Object Detector
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        • Vision Transformers Need Registers
        • What Do Self Supervised Vision Transformers Learn?
        • nGPT Normalized Transformer with Representation Learning on the Hypersphere
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        • Quantized Transfer Learning for Computer Vision Tutorial
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    Quantized Transfer Learning for Computer Vision Tutorial

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    authors Zafar Takhirov
    url https://pytorch.org/tutorials/intermediate/quantized_transfer_learning_tutorial.html
    2024-08-02
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