This repository contains everything you need to become proficient in ML/AI Research and Research Papers.
How to Make Best Use of ML/DL Research Papers?
Pic credits: ResearchGate
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Part 1 - How to solve Any ML System Design Problem
Link - Complete ML Research Papers Summarized Series
We will covering each and every Research Paper using 10 step framework —
| Model Name | Link |
|---|---|
| Transformer | Link |
| TransformerXL | Link |
| VGG | Link |
| Mask RCNN | Link |
| Masked Autoencoder | Link |
| BEiT | Link |
| BERT | Link |
| ColD Fusion | Link |
| ConvMixer | Link |
| Deep and Cross Network | Link |
| DenseNet | Link |
| DistilBERT | Link |
| DiT | Link |
| DocFormer | Link |
| Donut | Link |
| EfficientNet | Link |
| ELMo | Link |
| Entity Embeddings | Link |
| ERNIE-Layout | Link |
| FastBERT | Link |
| Fast RCNN | Link |
| Faster RCNN | Link |
| MobileBERT | Link |
| MobileNetV1 | Link |
| MobileNetV2 | Link |
| MobileNetV3 | Link |
| RCNN | Link |
| ResNet | Link |
| ResNext | Link |
| SentenceBERT | Link |
| Single Shot MultiBox Detector (SSD) | Link |
| StructuralLM | Link |
| Swin Transformer | Link |
| TableNet | Link |
| TabTransformer | Link |
| Tabular ResNet | Link |
| TinyBERT | Link |
| Vision Transformer | Link |
| Wide and Deep Learning | Link |
| Xception | Link |
| XLNet | Link |
| AlexNet | Link |
| BART | Link |
| InceptionNetV2 and InceptionNetV3 | Link |
| InceptionNetV4 and InceptionResNet | Link |
| Layout LM | Link |
| Layout LM v2 | Link |
| Layout LM v3 | Link |
| Lenet | Link |
| LiLT | Link |
| Feature Pyramid Network | Link |
| Feature Tokenizer Transformer | Link |
| Focal Loss (RetinaNet) | Link |
| Paper Name | Simplified/Summarized Version |
|---|---|
| Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models | Link |
| Bag of Tricks for Efficient Text Classification | Link |
| Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models | Link |
| (QA)²: Question Answering with Questionable Assumptions | Link |
| QueryForm: A Simple Zero-shot Form Entity Query Framework | Link |
| Semi-supervised Sequence Learning | Link |
| Universal Language Model Fine-tuning for Text Classification | Link |
| DARTS: Differentiable Architecture Search | Link |
| RoBERTa: A Robustly Optimized BERT Pretraining Approach | Link |
| Generating Sequences With Recurrent Neural Networks | Link |
| Deep contextualized word representations | Link |
| Regularizing and Optimizing LSTM Language Models | Link |
| End-To-End Memory Networks | Link |
| Listen, Attend and Spell | Link |
| Well-Read Students Learn Better: On the Importance of Pre-training Compact Models | Link |
| Language Models are Few-Shot Learners | Link |
| Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context | Link |
| DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | Link |
| Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond | Link |
| LIMA: Less Is More for Alignment | Link |
| Efficient Neural Architecture Search via Parameter Sharing | Link |
| Tree of Thoughts: Deliberate Problem Solving with Large Language Models | Link |
| AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head | Link |
| FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance | Link |
| CodeT5+: Open Code Large Language Models for Code Understanding and Generation | Link |
| Unlimiformer: Long-Range Transformers with Unlimited Length Input | Link |
| Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4 | Link |
| PaLM: Scaling Language Modeling with Pathways | Link |
| Attention Is All You Need | Link |
| Denoising Diffusion Probabilistic Models | Link |
| ZeRO: Memory Optimizations Toward Training Trillion Parameter Models | Link |
| Wide Residual Networks | Link |
| FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness | Link |
| STaR: Bootstrapping Reasoning With Reasoning | Link |
| Meta-Gradient Reinforcement Learning | Link |
| Distilling the Knowledge in a Neural Network | Link |
| How to Fine-Tune BERT for Text Classification? | Link |
| Primer: Searching for Efficient Transformers for Language Modeling | Link |
| Training Compute-Optimal Large Language Models | Link |
| Learning Transferable Visual Models From Natural Language Supervision | Link |
| More Coming soon |
| Paper Name | Summarized and Simplified Version |
|---|---|
| NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis | Link |
| More Coming Soon |