[Paper Exploration] Adam: A Method for Stochastic Optimization
From optimization, to convex optimization, to first order optimization, to gradient descent, to accelerated gradient descent, to AdaGrad, to Adam.
[Paper Exploration] Deep Residual Learning for Image Recognition
The paper introduces a novel architecture called residual networks (ResNets), which significantly improves deep neural network training by using skip connections to mitigate the vanishing gradient problem. This approach achieved state-of-the-art performance on several benchmarks, including the ImageNet dataset, and has become foundational in modern deep learning applications.
[Paper Exploration] In-Depth Analysis of the Segment Anything Model (SAM)
A comprehensive exploration of Meta AI's Segment Anything Model (SAM), a foundation model designed to generalize across various segmentation tasks with minimal prompting, zero-shot and few-shot learning capabilities, and applications in a wide range of domains.