Jirasak Buranathawornsom's repositories

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AT82.01

Repository containing work related to the class AT82.01 Computer Programming for Data Science and Artificial Intelligence from AIT

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AT82.05-NLU-Project

Repository containing the code for reproduce the experiment for AIT's AT82.05 Artificial Intelligence: Natural Language Understanding

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Best-README-Template

An awesome README template to jumpstart your projects!

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DevToys

A Swiss Army knife for developers.

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Efficient-AI-Backbones

Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

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health-checks

Scripts that check the health of my computers.

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HyperbolicImageSegmentation

Hyperbolic Image Segmentation, CVPR 2022

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JuliaTutorials

Learn Julia via interactive tutorials!

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MICCAI22_ADN

The implementation of our MICCAI22 paper "Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT Scans".

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mildlyoverfitted

Paper implementations from scratch and machine learning tutorials

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model-vs-human

Benchmark your model on out-of-distribution datasets with carefully collected human comparison data (NeurIPS 2021 Oral)

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NLP

This is the repository for the course Natural Language Processing at Asian Institute of Technology. Mostly covering theoretical aspects of NLP and some coding assignments using PyTorch

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pytorch-toolbelt

PyTorch extensions for fast R&D prototyping and Kaggle farming

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RobustViT

[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

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ToMe

A method to increase the speed and lower the memory footprint of existing vision transformers.

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Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

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Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

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validations

blue kale validation repo for training

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