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
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 |