From 75bc2863b3f7b56de78d72046be742f66e64bfd2 Mon Sep 17 00:00:00 2001 From: ghaymah_dev Date: Tue, 3 Mar 2026 17:23:28 +0000 Subject: [PATCH] Add readme.md --- readme.md | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 readme.md diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..e200ef0 --- /dev/null +++ b/readme.md @@ -0,0 +1,66 @@ + ## بسم الله الرحمن الرحيم + + +### Variational Autoencoders (VAEs) +* [Auto-Encoding Variational Bayes (Kingma & Welling, ICLR 2014)](https://arxiv.org/abs/1312.6114) + +### Generative Adversarial Networks (GANs) +* [Generative Adversarial Networks (Goodfellow et al., NIPS 2014)](https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html) (Note: Original NIPS link - please verify access) +* [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGAN) (Radford et al., ICLR 2016)](https://arxiv.org/abs/1511.06434) + +### Sequence-to-Sequence Models & Attention +* [Sequence to Sequence Learning with Neural Networks (Sutskever et al., NIPS 2014)](https://arxiv.org/abs/1409.3215) +* [Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et al., ICLR 2015)](https://arxiv.org/abs/1409.0473) +* [Attention Is All You Need (Transformer) (Vaswani et al., NIPS 2017)](https://arxiv.org/abs/1706.03762) + +### Optimizers & Normalization +* [Adam: A Method for Stochastic Optimization (Kingma & Ba, ICLR 2015)](https://arxiv.org/abs/1412.6980) +* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Ioffe & Szegedy, ICML 2015)](http://proceedings.mlr.press/v37/ioffe15.html) + +### Computer Vision Architectures +* [Going Deeper with Convolutions (GoogLeNet/Inception) (Szegedy et al., CVPR 2015)](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html) +* [Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet) (Simonyan & Zisserman, ICLR 2015)](https://arxiv.org/abs/1409.1556) +* [Deep Residual Learning for Image Recognition (ResNet) (He et al., CVPR 2016)](https://arxiv.org/abs/1512.03385) +* [You Only Look Once: Unified, Real-Time Object Detection (YOLO) (Redmon et al., CVPR 2016)](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html) + +### Normalization & Regularization (Continued) +* [Layer Normalization (Ba et al., NIPS 2016)](https://arxiv.org/abs/1607.06450) + +### Key 2017-2020 Papers +* [Attention Is All You Need (Vaswani et al., NIPS 2017)](https://arxiv.org/abs/1706.03762) +* [U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger et al., MICCAI 2015)](https://arxiv.org/abs/1505.04597) +* [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., NAACL 2019)](https://arxiv.org/abs/1810.04805) +* [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) (Dosovitskiy et al., ICLR 2021)](https://arxiv.org/abs/2010.11929) + +### Recent Foundational Models (2021-2023) +* [Highly accurate protein structure prediction with AlphaFold (Jumper et al., Nature 2021)](https://www.nature.com/articles/s41586-021-03819-2) +* [CLIP: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., ICML 2021)](https://arxiv.org/abs/2103.00020) +* [Denoising Diffusion Probabilistic Models (DDPM) (Ho et al., NeurIPS 2020)](https://arxiv.org/abs/2006.11239) +* [High-Resolution Image Synthesis with Latent Diffusion Models (Stable Diffusion / LDM) (Rombach et al., CVPR 2022)](https://arxiv.org/abs/2112.10752) +* [Learning Transferable Visual Models From Natural Language Supervision (CLIP) (Radford et al., ICML 2021)](https://arxiv.org/abs/2103.00020) + +### Large Language Models (LLMs) +* [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., NAACL 2019)](https://arxiv.org/abs/1810.04805) +* [Language Models are Few-Shot Learners (GPT-3) (Brown et al., NeurIPS 2020)](https://arxiv.org/abs/2005.14165) +* [Training language models to follow instructions with human feedback (InstructGPT) (Ouyang et al., NeurIPS 2022)](https://arxiv.org/abs/2203.02155) +* [GPT-4 Technical Report (OpenAI, 2023)](https://arxiv.org/abs/2303.08774) +* [Llama 2: Open Foundation and Fine-Tuned Chat Models (Touvron et al., 2023)](https://arxiv.org/abs/2307.09288) +* [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., NAACL 2019)](https://arxiv.org/abs/1810.04805) - Duplicate removed, keep one. +* [Language Models are Few-Shot Learners (GPT-3) (Brown et al., NeurIPS 2020)](https://arxiv.org/abs/2005.14165) +* [Training language models to follow instructions with human feedback (InstructGPT) (Ouyang et al., NeurIPS 2022)](https://arxiv.org/abs/2203.02155) + +### Transformers Beyond Text +* [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT) (Dosovitskiy et al., ICLR 2021)](https://arxiv.org/abs/2010.11929) +* [High-Resolution Image Synthesis with Latent Diffusion Models (Stable Diffusion / LDM) (Rombach et al., CVPR 2022)](https://arxiv.org/abs/2112.10752) - Duplicate removed, keep under Generative Models. +* [Segment Anything (Kirillov et al., ICCV 2023)](https://arxiv.org/abs/2304.02643) +* [Generative Agents: Interactive Simulacra of Human Behavior (Park et al., 2023)](https://arxiv.org/abs/2304.03442) + +### Miscellaneous & Other Notable Papers +* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (Ioffe & Szegedy, ICML 2015)](http://proceedings.mlr.press/v37/ioffe15.html) - Duplicate removed, keep under Optimizers & Normalization. +* [Deep Residual Learning for Image Recognition (ResNet) (He et al., CVPR 2016)](https://arxiv.org/abs/1512.03385) +* [U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger et al., MICCAI 2015)](https://arxiv.org/abs/1505.04597) +* [Highly accurate protein structure prediction with AlphaFold (Jumper et al., Nature 2021)](https://www.nature.com/articles/s41586-021-03819-2) +* [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., NeurIPS 2022)](https://arxiv.org/abs/2201.11903) +* [Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Yao et al., NeurIPS 2023)](https://arxiv.org/abs/2305.10601) +* [Visual Instruction Tuning (LLaVA) (Liu et al., NeurIPS 2023)](https://arxiv.org/abs/2304.08485) +