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Found 8 papers, 1 papers with code
EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python
This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with robust and accurate feature importance quantification and more reliable mechanistic interpretation of feature importance for prediction problems using fuzzy sets.
Contextual Intelligent Decisions: Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving
In this paper, we introduce a methodology (Expert-centered Driver Assessment) towards a fairer automatic road safety assessment of drivers' behaviours, taking into consideration behaviours as a response to contextual factors.
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture
Possible solutions to protect the privacy of users and avoid misuse of their identities are to: (1) extract anonymised facial features, namely action units (AU) from a database of images, discard the images and use AUs for processing and training, and (2) federated learning (FL) i. e. process raw images in users' local machines (local processing) and send the locally trained models to the main processing machine for aggregation (central processing).
Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach
A possible solution to improve the reliability of explanations is to combine results from multiple feature importance quantifiers from different machine learning approaches coupled with re-sampling.
Objective Assessment of Subjective Tasks in Crowdsourcing Applications
We discuss subjectivity in human centered tasks and present a filtering method that defines quality contributors, based on a set of objectively infused terms in a lexicon acquisition task.
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout
(b) We also introduce a simple meta-level dropout technique that reduces meta-level overfitting in several few-shot learning approaches.
ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016
A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age).
Ranked #2 on
Gender Prediction
on FotW Gender
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions.
