Search Results for author:
Found 15 papers, 3 papers with code
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
Importance attribution in neural networks by means of persistence landscapes of time series
We propose and implement a method to analyze time series with a neural network using a matrix of area-normalized persistence landscapes obtained through topological data analysis.
A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers
Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features and attributes.
Reconstruction of univariate functions from directional persistence diagrams
We describe a method for approximating a single-variable function $f$ using persistence diagrams of sublevel sets of $f$ from height functions in different directions.
Training Thinner and Deeper Neural Networks: Jumpstart Regularization
Neural networks are more expressive when they have multiple layers.
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures.
Differential Replication in Machine Learning
When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates.
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability
Accountability of such solutions is a challenge for the auditors and the machine learning community.
Sampling Unknown Decision Functions to Build Classifier Copies
Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs.
Superseding Model Scaling by Penalizing Dead Units and Points with Separation Constraints
Dead points refers to data points that are mapped to zero during the forward pass of the network.
Copying Machine Learning Classifiers
We study model-agnostic copies of machine learning classifiers.
Towards Global Explanations for Credit Risk Scoring
We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.
Learning to count with deep object features
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective.
Error-Correcting Factorization
To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes.
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD
We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class.
