paint-brush
How to Implement ADA for Data Augmentation in Nonlinear Regression Modelsby@anchoring
125 reads

How to Implement ADA for Data Augmentation in Nonlinear Regression Models

by Anchoring
Anchoring HackerNoon profile picture

Anchoring

@anchoring

Anchoring provides a steady start, grounding decisions and perspectives in...

November 14th, 2024
Read on Terminal Reader
Read this story in a terminal
Print this story
Read this story w/o Javascript
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

The ADA algorithm generates minibatches for nonlinear regression models by selecting samples, computing projections, and applying transformations based on predefined criteria. This process enhances data diversity with each minibatch, improving model robustness and performance.
featured image - How to Implement ADA for Data Augmentation in Nonlinear Regression Models
1x
Read by Dr. One voice-avatar

Listen to this story

Anchoring HackerNoon profile picture
Anchoring

Anchoring

@anchoring

Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence.

About @anchoring
LEARN MORE ABOUT @ANCHORING'S
EXPERTISE AND PLACE ON THE INTERNET.
0-item

STORY’S CREDIBILITY

Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Authors:

(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);

(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);

(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch).

Abstract and 1 Introduction

2 Background

2.1 Data Augmentation

2.2 Anchor Regression

3 Anchor Data Augmentation

3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure

3.3 Algorithm

4 Experiments and 4.1 Linear synthetic data

4.2 Housing nonlinear regression

4.3 In-distribution Generalization

4.4 Out-of-distribution Robustness

5 Conclusion, Broader Impact, and References


A Additional information for Anchor Data Augmentation

B Experiments

3.3 Algorithm

Finally, in this section, we present the ADA algorithm step by step (Algorithm 1) to generate minibatches of data that can be used to train neural networks (or any other nonlinear regressor) by any stochastic gradient descent method. As discussed previously, we propose to repeat the augmentation with different parameter combinations for each minibatch.


image


image


This paper is available on arxiv under CC0 1.0 DEED license.


L O A D I N G
. . . comments & more!

About Author

Anchoring HackerNoon profile picture
Anchoring@anchoring
Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence.

TOPICS

THIS ARTICLE WAS FEATURED IN...

Permanent on Arweave
Read on Terminal Reader
Read this story in a terminal
 Terminal
Read this story w/o Javascript
Read this story w/o Javascript
 Lite
Also published here
Hackernoon
X
Threads
Bsky
X REMOVE AD