Essential AI Papers & Resources - A Curated Collection
Navigating the AI Paper Landscape
The field of AI moves at a dizzying pace, with dozens of new papers published daily. But which ones are truly worth your time? I’ve compiled this collection of papers that have shaped my understanding of AI and machine learning—from foundational concepts to cutting-edge innovations. Whether you’re looking to enter the field or deepen your expertise, these resources should help guide your journey.
Essential Papers to Know
| Paper Title | Resources & Notes |
|---|---|
| RAG/LoRA Techniques | Building blocks for efficient fine-tuning and retrieval |
| Demucs | Audio source separation breakthrough |
| ML from Scratch Series | Transformers |
| Diffusion Models | |
| Comprehensive Resources | |
| Diffusion Models | Illustrated Guide |
| U-Net Paper: Biomedical Segmentation | |
| minDiffusion Implementation | |
| Score SDE Paper | |
| Unified Perspective Tutorial | |
| GAN Tutorial | Lilian’s Blog |
| DeepLearning.AI Specialization | |
| ControlNet | Paper |
| Code Implementation | |
| Adam Optimizer | The paper that changed how we train deep networks |
| Flash Attention | Memory-Efficient Attention |
| Focal vs Cross Entropy Loss | Detailed Analysis |
| Mamba | State space models as alternative to transformers |
| CLIP | Connecting vision and language |
| Wavenet | Pioneering audio generation |
| Neural Probabilistic Language Model | Foundations of modern NLP |
| Byte Pair Encoding | Essential for tokenization |
| BERT | The paper that revolutionized NLP |
| Attention Mechanisms | Bahdanau (2014) and Luong (2015) papers |
| VQ-VAE | Understanding Discretization Benefits |
| Soundstream | CNN Visualizations from 3Blue1Brown |
| CNN Basics with Image/Audio | |
| ML Crash Course | Part 1 / Part 2 / Part 3 / Part 4 / Part 5 |
| Karpathy’s makemore series | |
| Layer Normalization | Batch vs Layer Norm Explained |
| Audiogen | Audio generation breakthroughs |
| Attention is All You Need | Transformer Tutorial |
| Illustrated Transformer | |
| Residual Connections | Building Blocks of ResNet |
Must-Read Blogs
The best papers are often complemented by clear explanations from talented writers:
- Lil’log
- Jay Alammar
- Andrej Karpathy
- Colah’s Blog
- ML@Berkeley
- AI Summer
- Blog pages from DeepMind and OpenAI
Where to Find Papers
To stay current with emerging research:
- OpenReview.net
- ArXiv.org
- Google Scholar
Researchers Worth Following
These voices consistently contribute groundbreaking work:
- Jürgen Schmidhuber
- Andrej Karpathy
- Ilya Sutskever
- Ian Goodfellow
- Yann LeCun
- Yoshua Bengio
- Geoffrey Hinton
- Alexei Efros
- Andrew Ng
- Sharon Zhou
What papers or resources have you found most valuable in your AI journey? I’d love to expand this list with your recommendations!