Search Results for author:
Found 11 papers, 4 papers with code
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
In this paper, we propose IGANN Sparse, a novel machine learning model from the family of generalized additive models, which promotes sparsity through a non-linear feature selection process during training.
Generative AI
Based on that, we introduce limitations of current generative AI and provide an agenda for Business & Information Systems Engineering (BISE) research.
A Survey of Text Representation Methods and Their Genealogy
In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication.
Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations
The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines.
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models.
Survey and Systematization of 3D Object Detection Models and Methods
Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection.
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV).
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems.
A survey of image labelling for computer vision applications
Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems.
Machine learning and deep learning
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning.
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds
Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions within different domains.
