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+ ## 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)
+