368 citations · 1.2k across the 16 of their papers we have counts for
18 papers
Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities
Xintong Shi, Wenzhi Cao, Sebastian Raschka
In recent times, deep neural networks achieved outstanding predictive performance on various classification and pattern recognition tasks. However, many real-world prediction probl…
Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course
Sebastian Raschka
Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources. While conventional lectures provide students with important in…
Ten Quick Tips for Deep Learning in Biology
Benjamin D. Lee, Anthony Gitter, Casey S. Greene +17
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that…
When Few-Shot Learning Meets Video Object Detection
Zhongjie Yu, Gaoang Wang, Lin Chen +2
Different from static images, videos contain additional temporal and spatial information for better object detection. However, it is costly to obtain a large number of videos with…
Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness
Kaiping Chen, Sang Jung Kim, Qiantong Gao +1
Recent years have witnessed an explosion of science conspiracy videos on the Internet, challenging science epistemology and public understanding of science. Scholars have started t…
Looking back to lower-level information in few-shot learning
Zhongjie Yu, Sebastian Raschka
Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rul…
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Sebastian Raschka, Joshua Patterson, Corey Nolet
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the to…
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
Sebastian Raschka, Benjamin Kaufman
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry.…
PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
Vahid Mirjalili, Sebastian Raschka, Arun Ross
Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this…
FlowSAN: Privacy-enhancing Semi-Adversarial Networks to Confound Arbitrary Face-based Gender Classifiers
Vahid Mirjalili, Sebastian Raschka, Arun Ross
Privacy concerns in the modern digital age have prompted researchers to develop techniques that allow users to selectively suppress certain information in collected data while allo…
Rank consistent ordinal regression for neural networks with application to age estimation
Wenzhi Cao, Vahid Mirjalili, Sebastian Raschka
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as mult…
Automated discovery of GPCR bioactive ligands
Sebastian Raschka
While G-protein coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Due to th…
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
Sebastian Raschka
The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This…
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
Vahid Mirjalili, Sebastian Raschka, Arun Ross
Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images. SANs are convolutional autoencoders that perturb face images such that…
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
Vahid Mirjalili, Sebastian Raschka, Anoop Namboodiri +1
In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transform…
MusicMood: Predicting the mood of music from song lyrics using machine learning
Sebastian Raschka
Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or resta…
An Overview of General Performance Metrics of Binary Classifier Systems
Sebastian Raschka
This document provides a brief overview of different metrics and terminology that is used to measure the performance of binary classification systems.
Naive Bayes and Text Classification I - Introduction and Theory
Sebastian Raschka
Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in t…