Learning, Data set, Data, Machine learning, Data analysis, Algorithm

Representation quality and the complexity of learning

On Sep 25, 2020
@kastnerkyle shared
RT @wfwhitney: New paper! We propose to measure the quality of learned representations using the complexity of finding a nearly-optimal predictor on a downstream task. Blog: https://t.co/3HrGoWgQA7 Paper: https://t.co/r7PHNsVIub Library: https://t.co/oO3pfGIUYn
Open

Writing the expected loss of running algorithm on a dataset with points as , the SDL measure of representation quality is We show in the paper that MDL is a special case of SDL which assumes that the true distribution of is a delta mass, which is to say that has no randomness at all. MDL ...

wp.nyu.edu
On Sep 25, 2020
@kastnerkyle shared
RT @wfwhitney: New paper! We propose to measure the quality of learned representations using the complexity of finding a nearly-optimal predictor on a downstream task. Blog: https://t.co/3HrGoWgQA7 Paper: https://t.co/r7PHNsVIub Library: https://t.co/oO3pfGIUYn
Open

Representation quality and the complexity of learning

Representation quality and the complexity of learning

Writing the expected loss of running algorithm on a dataset with points as , the SDL measure of representation quality is We show in the paper that MDL is a special case of SDL which ...

Get Machine Learning Training Data Using The Lionbridge Method [A How-To Guide]

Get Machine Learning Training Data Using The Lionbridge Method [A How-To Guide]

In the field of machine learning, training data preparation is one of the most important and time-consuming tasks. In fact, many data scientists claim that a large portion of data science ...

The 4 Machine Learning Models Imperative for Business Transformation

The 4 Machine Learning Models Imperative for Business Transformation

Deploying machine learning models which predict an outcome across a business is no easy feat. That’s particularly true given that data science is an industry in which hype and promise are ...

How to handle Imbalanced Classification Problems in machine learning?

How to handle Imbalanced Classification Problems in machine learning?

Guide explaining various ways to handle imbalanced classification problem in machine learning. This includes over sampling, undersampling and boosting

Towards Learning Convolutions from Scratch

Towards Learning Convolutions from Scratch

Convolution is one of the most essential components of architectures used in computer vision. As machine learning moves towards reducing the expert bias and learning it from data, a natural ...

Interpretability is crucial for trusting AI and machine learning

Interpretability is crucial for trusting AI and machine learning

However, with the recent advances in machine learning and artificial intelligence, models have become very complex, including complex deep neural networks and ensembles of different ...

Continuous Learning AI in Radiology: Implementation Principles and Early Applications

Continuous Learning AI in Radiology: Implementation Principles and Early Applications

Learning Objectives: After reading the article and taking the test, the reader will be able to: ■ Identify how static artificial intelligence (AI) algorithm performance degrades over time, ...

Solving Data Challenges In Machine Learning With Automated Tools

Solving Data Challenges In Machine Learning With Automated Tools

Percentage of time allocated to machine learning projects (source) This article will talk about the most common data preparation challenges that require data scientists and machine learning ...

Build, Train, and Deploy a Machine Learning Model

Build, Train, and Deploy a Machine Learning Model

What to learn how to build, train, and deploy a machine learning model with Amazon SageMaker? Learn how to build, train, and deploy a machine learning model with Amazon SageMaker in 10 ...