Entropy from Machine Learning
We translate the problem of calculating the entropy of a set of binary configurations/signals into a sequence of supervised classification tasks. Subsequently, one can use virtually any machine learning classification algorithm for computing entropy. This procedure can be used to compute entropy, and consequently the free energy directly from a set of Monte Carlo configurations at a given temperature. As a test of the proposed method, using an off-the-shelf machine learning classifier we reproduce the entropy and free energy of the 2D Ising model from Monte Carlo configurations at various temperatures throughout its phase diagram. Other potential applications include computing the entropy of spiking neurons or any other multidimensional binary signals.
Code (1)
Tasks
BIG-bench Machine LearningGeneral ClassificationSimilar Papers 제목 키워드 기반
Quantum Cross Entropy and Maximum Likelihood Principle
Quantum machine learning is an emerging field at the intersection of machine learning and quantum computing. Classical cross entropy plays a central role in machine learning. We define its quantum generalization, the qua…
BIG-bench Machine LearningQuantum Machine LearningRelationQuantum Data Compression and Quantum Cross Entropy
The emerging field of quantum machine learning has the potential of revolutionizing our perspectives of quantum computing and artificial intelligence. In the predominantly empirical realm of quantum machine learning, a t…
BIG-bench Machine LearningData CompressionQuantum Machine LearningMachine Learning Predictors for Min-Entropy Estimation
This study investigates the application of machine learning predictors for min-entropy estimation in Random Number Generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is ess…
Laconic Image Classification: Human vs. Machine Performance
We propose laconic classification as a novel way to understand and compare the performance of diverse image classifiers. The goal in this setting is to minimise the amount of information (aka. entropy) required in indivi…
Classificationimage-classificationImage ClassificationInstance-based entropy fuzzy support vector machine for imbalanced data
Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy …
BIG-bench Machine LearningDiversityimbalanced classification