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Found 17 papers, 9 papers with code
Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities
However, while earlier experiments showed that CORAL's rank consistency is beneficial for performance, it is limited by a weight-sharing constraint in a neural network's fully connected output layer, which may restrict the expressiveness and capacity of a network trained using CORAL.
Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course
Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources.
Ten Quick Tips for Deep Learning in Biology
In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
When Few-Shot Learning Meets Video Object Detection
We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color and Brightness
Recent years have witnessed an explosion of science conspiracy videos on the Internet, challenging science epistemology and public understanding of science.
Looking back to lower-level information in few-shot learning
In this work, we propose the utilization of lower-level, supporting information, namely the feature embeddings of the hidden neural network layers, to improve classifier accuracy.
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future.
PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e. g., age and race), while allowing for other types of attributes to be extracted (e. g., gender).
FlowSAN: Privacy-enhancing Semi-Adversarial Networks to Confound Arbitrary Face-based Gender Classifiers
In this regard, Semi-Adversarial Networks (SAN) have recently emerged as a method for imparting soft-biometric privacy to face images.
Rank consistent ordinal regression for neural networks with application to age estimation
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 multi-category cross-entropy.
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Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
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.
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images.
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject.
MusicMood: Predicting the mood of music from song lyrics using machine learning
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 restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively.
An Overview of General Performance Metrics of Binary Classifier Systems
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
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 the fields of document classification and disease prediction.
