Understanding the Difficulty of Training Transformers
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Updated
May 31, 2022 - Python
Understanding the Difficulty of Training Transformers
Simple implementation of the LSUV initialization in keras
Simple implementation of the LSUV initialization in PyTorch
Class Normalization for Continual Zero-Shot Learning
Short description for quick search
[NeurIPS 2022] Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again by Ajay Jaiswal*, Peihao Wang*, Tianlong Chen, Justin F Rousseau, Ying Ding, Zhangyang Wang
This repository is for numerical experiments with convolutional neural networks, in particular for testing initialization procedures. The training data is the CIFAR10 dataset and the CNN-architecture is the FitNet-1 from ROMERO, Adriana, et al. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550, 2014.
Structured Initialization for Attention in Vision Transformers
Odoo add-on that processes the content of one or more configuration folders.
Warmup initialisation procedure for RNNs
This Python program lets you create and use a multi-sided die, just like the ones you’d use in board games. With this class, you can make dice with any number of sides, roll them to get a random number, and set or check the current value showing on the die. The program also includes a simple example to show how it works.
Repo for the course project
Code to reproduce the paper "Deconstructing the Goldilocks Zone of Neural Network Initialization"
My solution to an assignment on neural network initializers and optimizers. Contains some of the most popular approaches such as Xavier/He initialization and SGD, Momentum, AdaGrad, AdaDelta and Adam optimizers.
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