Search Results for author:
Found 4 papers, 1 papers with code
A survey and taxonomy of loss functions in machine learning
This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.
Maximum entropy exploration in contextual bandits with neural networks and energy based models
This provides practitioners with new techniques that perform well in static and dynamic settings, and are particularly well suited to non-linear scenarios with continuous action spaces.
Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework
With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased.
Direct optimisation of the discovery significance when training neural networks to search for new physics in particle colliders
We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider.
High Energy Physics - Experiment
