auum

Over the last few years a number of open source machine learning 
projects have emerged that are capable of raising the frame rate of 
source video to 60 frames per second and beyond, producing a smoothed, 
'hyper-real' look.

Why It’s Very Difficult to Create AI-Based Slow Motion

Explore how hyperparameter tuning in ADA affects model performance, robustness, and optimization with a 1D cosine data example and minibatch augmentation.

How Hyperparameter Tuning Enhances Anchor Data Augmentation for Robust Regression

ADA (Anchor Data Augmentation) offers a novel approach to data augmentation by mixing multiple samples based on clustering. 

ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness

This paper presents the ADA algorithm for generating minibatches in nonlinear regression models, using stochastic gradient descent. 

How to Implement ADA for Data Augmentation in Nonlinear Regression Models

ADA is a generalized version of C-Mixup that mixes multiple samples based on cluster membership, preserving nonlinear relationships in augmented regression data

Anchor Data Augmentation as a Generalized Variant of C-Mixup

Anchor Data Augmentation (ADA) is a domain-agnostic method for regression, using clustering to improve generalization with minimal computational cost.

Anchor Data Augmentation (ADA): A Domain-Agnostic Method for Enhancing Regression Models

This paper explores various data augmentation methods, from human-designed domain-specific transformations to automated techniques using reinforcement learning.

A Comprehensive Overview of Data Augmentation Methods

This paper introduces Anchor Data Augmentation (ADA), a novel algorithm for enhancing nonlinear over-parameterized regression models. 

A Novel Anchor Data Augmentation Algorithm for Nonlinear Models

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