Comments (1)
romanngg
commented on June 6, 2024
Mathematically I think we're doing what you've wrote, but we implement it with Cholesky factorization, so instead of
mean_predict.analytical result = kernel_cov.T.dot(Kff_inv).dot(train_ys)we do something like
import jax.scipy as sp
c, _ = sp.linalg.cho_factor(kernel_train + noise_scale * noise_scale * np.mean(np.trace(kernel_train)) * np.eye(len(train_xs)))
Kff_inv_dot_train_ys = sp.linalg.cho_solve(c, train_ys)
mean_predict.analytical result = kernel_cov.T.dot(Kff_inv_dot_train_ys)This could give slightly different results from np.linalg.inv, but is faster. Could this explain the difference, or you get huge discrepancy?
from neural-tangents.
Related Issues (20)
- Question: Relu Kernel Computation HOT 3
- Question: Connection MLE "parametrized" GP in infinite Width Limit vs minimizing MSE "parametrized" Kernel in infinite Width HOT 4
- Question regarding OOM issues HOT 3
- Question regarding lr in Neural Tangents Cookbook
- eNTK implementation uses deprecated xla attribute HOT 2
- Colab notebooks issue HOT 2
- How to obtain aleatoric uncertainty? HOT 2
- How to compute the empirical after kernel? HOT 1
- pip install issues HOT 2
- Erf function goes beyond [-1,1] HOT 2
- using stax.Cos(a=1.0, b=1.0, c=0.0) to get kernel from conv layer gives error HOT 2
- NTK is not PD
- stax.serial PSDness HOT 1
- How to use batch to gradient_descent_mse_ensemble ? HOT 1
- NTK/NNGP behavior in the infinite regime when weights are drawn from Gaussians with high standard deviation HOT 7
- NKT_mean output Nan, when the number of training sample is increased HOT 3
- Inefficient jacobian computation for embedding layers. HOT 1
- Question regarding the cookbook
- Calling the empirical kernel function with different parameters returns same result
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from neural-tangents.