What is non-Euclidean data? - Artificial Intelligence Stack Exchange
Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics.
Do you know any examples of geometric deep learning used in …
To call the two categories Euclidean and non-euclidean is inaccurate. The initial trend in machine learning followed a Cartesian paradigm, one locked into 90° angles in data structures. The free-form graph is non-cartesian but still just as Euclidean, as Euclid worked with angles other than 90° …
definitions - What is geometric deep learning? - Artificial ...
$\begingroup$ Manifold is easy to understand if your understand euclidean space. Euclidean space is a space of n-dimensional vectors of real numbers. Manifold is a space (set of) vectors that for any point of it small area around it could be transformed (smoothly and reversibly) to ball of Euclidean space.
What is the purpose of an activation function in neural networks?
2018年3月3日 · I hardly think you can find any physical world phenomenon which follows linearity straightforwardly. So you need a non-linear function that can approximate the non-linear phenomenon. Also, a good intuition would be any decision boundary or a function is a linear combination of polynomial combinations of the input features (so ultimately non ...
How Euclidean distance algorithm calculate two different face …
2019年11月25日 · Simply put, Euclidean distance measures how far away two items are (see Neil Slater's comment). In order to apply this to a pattern recognition task, you will need to convert the items to compare (in your case images of faces) into feature vectors (ie lists of numerical values), and then you do a pairwise comparison to work out how distant two faces are.
definitions - Explaining AI to Non-Technical Individuals - Artificial ...
2021年8月10日 · There are a lot of ways to describe "Artificial Intelligence". An artifact that "makes a decision"; This form of automation/computing/AI goes back to neolithic times.
How is the constraint $\|f(x)\|_{2}=1$ enforced for the embedding …
The constraint is enforced with bespoke code. If FaceNet was implemented in NumPy, and the embedding layer vector (pre-constraint) was in the NumPy array h, then the code might look like:
What are the differences between A* and greedy best-first search?
2019年8月30日 · In summary, greedy BFS is not complete, not optimal, has a time complexity of $\mathcal{O}(b^m)$ and a space complexity which can be polynomial. A* is complete, optimal, and it has a time and space complexity of $\mathcal{O}(b^m)$. So, in general, A* uses more memory than greedy BFS. A* becomes impractical when the search space is huge.
What is the difference between the notations $\\|x\\|_1, \\|x\\|_2
2020年1月27日 · $\|\textbf{x}\|_2 = \sqrt{x_1^2 + \dots + x_n^2}$ denotes the Euclidean norm on an N-D Euclidean space, which is a result of the Pythagorean theorem (the shortest distance between two points). Share Improve this answer
Frequent 'deep-learning' Questions - Artificial Intelligence Stack …
What is non-Euclidean data? ... use in an environment with a continous state space a discrete action space.