Machine Learning Paper Reviews

ViT: An Image is Worth 16x16 Words

Alexey Dosovitskiy, Lucas Beyer, +10

Vision Transformer explained with original paper equations, tensor shapes, runnable PyTorch code, and real pretrained image classification.

A Survey of Techniques for Optimizing Transformer Inference

Krishna Teja Chitty-Venkata, Sparsh Mittal, +3

Survey of transformer inference optimization: pruning, quantization, knowledge distillation, neural architecture search, and hardware acceleration.

SURF: Speeded Up Robust Features

Herbert Bay, Tinne Tuytelaars, +1

SURF is a fast and robust algorithm for local feature detection and description, used in object recognition, image registration, and 3D reconstruction.

Swin Transformer: Hierarchical ViT with Shifted Windows

Ze Liu, Yutong Lin, +6

Swin Transformer: hierarchical Vision Transformer using shifted windows for efficient image classification, object detection, and segmentation.

CLIP: Visual Models via Language Supervision

Alec Radford, Jong Wook Kim, +10

CLIP explained: contrastive learning on 400M image-text pairs enables zero-shot image classification and powerful vision-language understanding.

Making Deep Learning Go Brrrr From First Principles

Horace He

Deep learning performance optimization from first principles. Learn to identify compute-bound, memory-bound, and overhead bottlenecks with fusion techniques.

Attention Is All You Need

Ashish Vaswani, Noam Shazeer, +6

Deep dive into the Transformer architecture that revolutionized NLP. Understand self-attention, multi-head attention, and positional encoding.

Data Movement Is All You Need: Optimizing Transformers

Andrei Ivanov, Nikoli Dryden, +3

Analysis of transformer performance bottlenecks caused by data movement. Learn optimization strategies for memory-bound operations on GPUs.

ResNet

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, +2

ResNet analysis: how skip connections and residual learning solved the degradation problem, enabling training of 100+ layer neural networks.