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Found 10 papers, 8 papers with code
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
Generalized test utilities for long-tail performance in extreme multi-label classification
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
Propensity-scored Probabilistic Label Trees
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels.
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
Online probabilistic label trees
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner without any prior knowledge about the number of training instances, their features and labels.
Efficient Set-Valued Prediction in Multi-Class Classification
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
ViZDoom Competitions: Playing Doom from Pixels
The results of the competition lead to the conclusion that, although reinforcement learning can produce capable Doom bots, they still are not yet able to successfully compete against humans in this game.
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.
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