TPs de la materia 22.67 - Redes Neuronales.
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Updated
Jun 30, 2024 - Jupyter Notebook
TPs de la materia 22.67 - Redes Neuronales.
[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
Image Classification With Vision Transformer
Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)
El proyecto se centra en la destilación de conocimiento y técnicas de explicabilidad para mejorar el rendimiento de redes neuronales en imágenes naturales.
ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch
Official PyTorch Implementation of Guarding Barlow Twins Against Overfitting with Mixed Samples
Comparison of MLP, kNN, NC models trained on CIFAR-100.
Vision transformer and CNN implementations for image classification using PyTorch.
Plug-and-play collaboration between specialized Tsetlin machines
Pytorch based tools for experimenting with the cifar-10 and cifar-100 datasets
Mini-silly image classifier UI with tensorflow and PyQT5.
Code for the neural architecture search methods contained in the paper Efficient Forward Neural Architecture Search
Training using an alternative approach: forward-thinking
ISEF 2023 (TEAM CANADA) PROJECT. Find the complete documentation and code in the README file linked here and below.
We implement NNCLR and a novel clustering-based technique for contrastive learning that we call KMCLR. We show that applying a clustering technique to obtain prototype embeddings and using these prototypes to form positive pairs for contrastive loss can achieve performances on par with NNCLR on CIFAR-100 while storing 0.4% of the number of vectors.
VGG models from ILSVRC 2014
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