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10/24/2022
Occam learning
We discuss probabilistic neural network models for unsupervised learning...
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02/01/2022
Quantifying Relevance in Learning and Inference
Learning is a distinctive feature of intelligent behaviour. High-through...
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12/17/2021
A random energy approach to deep learning
We study a generic ensemble of deep belief networks which is parametrize...
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08/02/2020
Statistical Inference of Minimally Complex Models
Finding the best model that describes a high dimensional dataset, is a d...
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06/12/2020
Characterising authors on the extent of their paper acceptance: A case study of the Journal of High Energy Physics
New researchers are usually very curious about the recipe that could acc...
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06/08/2020
Optimal Work Extraction and the Minimum Description Length Principle
We discuss work extraction from classical information engines (e.g., Szi...
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11/05/2019
Thermodynamic Computing
The hardware and software foundations laid in the first half of the 20th...
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03/01/2019
On the complexity of logistic regression models
We investigate the complexity of logistic regression models which is def...
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09/03/2018
Minimum Description Length codes are critical
Learning from the data, in Minimum Description Length (MDL), is equivale...
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05/02/2017
Influence of Reviewer Interaction Network on Long-term Citations: A Case Study of the Scientific Peer-Review System of the Journal of High Energy Physics
A `peer-review system' in the context of judging research contributions,...
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02/24/2017
The Stochastic complexity of spin models: How simple are simple spin models?
Simple models, in information theoretic terms, are those with a small st...
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03/03/2016
