Hyperparameter optimization in Machine Learning Part-1: Algorithms

March 16, 2023

Hyperparameter optimization in Machine Learning Part-1: Algorithms

In these two article series, we are going to learn about Hyperparameter Optimization in Machine learning. Part-1 is about the theoretical foundation and algorithms, while Part-2 will be devoted to practical aspects like libraries, frameworks, and services for hyperparameter optimization. 

Hyperparameter optimization for machine learning is a particular use case of Black box optimization. Now, What are hyperparameters and what is black box optimization? Let's start with learning about Black Box optimization. When we have a function with some known parameters but you can't really peer inside the function a, you want to find the best setting options for these parameters. The only way to assess their efficiency is to set the parameters appropriately and run an experimental process, which is considered quite expensive. Running this process gives objective value or performance measure through which you can find the best parameters. The system is relatively opaque through which evaluation can be costly.

Let's take an example of an object detection model. In the image below you can observe the various hyperparameters associated with the model, training, and suppression:

In machine learning, hyperparameter optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. One cannot learn hyperparameters during the training an we need to set them in advance. Afterwards, they can be formulated as black-box optimization. AutoML (the process of automating the time-consuming, iterative tasks of machine learning model development) is also a black-box optimization problem.

There are two main algorithmic approaches for black-box optimization:

  1. Bayesian Optimization
  2. Simple probabilistic algorithm

Bayesian Optimization

Bayesian Optimization is one among many popular approaches. It is the best approach for a broad range of problems. 

As an example, we take a single dimension parameter space and plot the objective function on the y-axis. In the Bayesian approach, we try to model the objective function. The green points show the values of parameters and the black line is an objective function but it is hidden from us. So we try to come up with some sort of region in which the optimization function lives (which is shaded in blue in the above figure). Now, this problem is turned into an explore-exploit tradeoff problem which we call a multi-arm bandit problem.

Now the question arises what is a multi-arm bandit problem? In probability theory and machine learning, the multi-armed bandit problem is a problem in which a fixed set of resources must be allocated between competing choices in a way that maximizes their expected gain when each choice's properties are only partially known at the time of allocation and may become better understood as time passes or by allocating resources to the choice. 

One standard strategy is to find the maximum point in the right envelope which is shown as the star in the diagram and use that value of the parameter. As we can see,  the objective function value for the star parameter is not the best so we change and update this picture.

Using Gaussian Process to model the function 

Gaussian Process is state-of-the-art for many small datasets but there are other choices too for modeling the objective function. In probability theory and statistics, a Gaussian process is a stochastic process, such that every finite collection of those random variables has a multivariate normal distribution. The distribution of a Gaussian process is the joint distribution of all those (infinitely many) random variables, and as such, it is a distribution over functions with a continuous domain, e.g. time or space. 

Simple Probabilistic Algorithm

One approach can be a simple random search (Random Search sets up a grid of hyperparameter values and selects random combinations to train the model and score) but there are techniques that are better:

  • Uniform Random Sampling
  • Ball Sampling (probabilistic gradient-free hill-climbing)
  • Linear Combination Sampling: Create a new point with a linear combination

Automated Early Stopping

For some problems like Machine Learning, the optimization is not a complete black box as we have access to additional information i.e. information found during training.

As depicted in the accuracy vs training time plot, in the above figure, we have some solid lines which depict the training completed and a dotted line showing the ongoing epoch. But comparing these two we can make the decision of stopping early if we are sure that this combination of parameters is not going to give optimal results. 

Closing Thought:- We encourage the readers to create a neural network and do hyperparameter optimization on a free GPU Trial on E2E Cloud. For getting your free credits please contact:- sales@e2enetworks.com

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

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  • It can also help in completing DNA sequences.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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So, read on to know more.

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What is Q-Learning Algorithm?

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Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

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What does GAUDI do?

GAUDI can perform multiple functions –

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  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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