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Found 17 papers, 6 papers with code
A practical guide and software for analysing pairwise comparison experiments
Most popular strategies to capture subjective judgments from humans involve the construction of a unidimensional relative measurement scale, representing order preferences or judgments about a set of objects or conditions.
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Data augmentation is rapidly gaining attention in machine learning.
A mixture of experts model for predicting persistent weather patterns
Weather and atmospheric patterns are often persistent.
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners.
Towards an Integrative Educational Recommender for Lifelong Learners
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning.
Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization
In this paper we propose ASAP, an active sampling algorithm based on approximate message passing and expected information gain maximization.
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement
This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement.
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4, 000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets.
PEEK: A Large Dataset of Learner Engagement with Educational Videos
One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets.
Learning PAC-Bayes Priors for Probabilistic Neural Networks
We experiment on 6 datasets with different strategies and amounts of data to learn data-dependent PAC-Bayes priors, and we compare them in terms of their effect on test performance of the learnt predictors and tightness of their risk certificate.
Progress in Self-Certified Neural Networks
We then show that in data starvation regimes, holding out data for the test set bounds adversely affects generalisation performance, while self-certified strategies based on PAC-Bayes bounds do not suffer from this drawback, proving that they might be a suitable choice for the small data regime.
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All
Artifical Intelligence (AI) in Education has great potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning.
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?
The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos.
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections.
Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models
Governments around the world aspire to ground decision-making on evidence.
Auditing Large Language Models for Enhanced Text-Based Stereotype Detection and Probing-Based Bias Evaluation
Recent advancements in Large Language Models (LLMs) have significantly increased their presence in human-facing Artificial Intelligence (AI) applications.
The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models
In this work, we introduce Correlational Explanatory Faithfulness (CEF), a metric that can be used in faithfulness tests based on input interventions.
