/explore
|
You are here 
|
marc-b-reynolds.github.io | ||
| | | | |
nickhar.wordpress.com
1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... |
|
| | | | | ||
| | | | |
blog.georgeshakan.com
Principal Component Analysis (PCA) is a popular technique in machine learning for dimension reduction. It can be derived from Singular Value Decomposition (SVD) which we will discuss in this post. We will cover the math, an example in python, and finally some intuition. The Math SVD asserts that any $latex m \times d$ matrix $latex... |
|
| | | | | ||
| | | | |
entangledlogs.com
To visualize quaternions in the fanciest way, visit Ben eater, Quaternion. Euler angles suffer from a problem of gimbal lock. When rotating around a 3-perpendicular axis in euclidean space, if either two of these axes align i.e becomes parallel, it causes gimbal lock. Once the object is locked, the object will lose one degree of freedom for rotation. This video provides an intuitive explanation of the problem. Pitfalls When converting the Euler angle to a quaternion, it will lose some information. |
|
| | | | | ||
| | | | |
www.frontiersin.org
In transferable black-box attacks, adversarial samples remain adversarial across multiple models and are more likely to attack unknown models. From this view... |
|
| | | |||
