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Found 51 papers, 13 papers with code
A Toolbox for Modelling Engagement with Educational Videos
With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future.
Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models
Governments around the world aspire to ground decision-making on evidence.
Social AI and the Challenges of the Human-AI Ecosystem
In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI.
Exploration via Epistemic Value Estimation
We propose epistemic value estimation (EVE): a recipe that is compatible with sequential decision making and with neural network function approximators.
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.
TransductGAN: a Transductive Adversarial Model for Novelty Detection
Transductive novelty detection on the other hand has only witnessed a recent surge in interest, it not only makes use of the negative class during training but also incorporates the (unlabeled) test set to detect novel examples.
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Current PAC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate.
Chaining Value Functions for Off-Policy Learning
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its experience.
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.
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems
In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas.
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution
Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning.
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.
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.
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.
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.
Upper and Lower Bounds on the Performance of Kernel PCA
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades.
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings
Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent.
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.
Tighter risk certificates for neural networks
In the context of probabilistic neural networks, the output of training is a probability distribution over network weights.
PAC-Bayes Analysis Beyond the Usual Bounds
Specifically, we present a basic PAC-Bayes inequality for stochastic kernels, from which one may derive extensions of various known PAC-Bayes bounds as well as novel bounds.
PAC-Bayes unleashed: generalisation bounds with unbounded losses
We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions.
Predicting Engagement in Video Lectures
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners.
Correlated Feature Selection with Extended Exclusive Group Lasso
In many high dimensional classification or regression problems set in a biological context, the complete identification of the set of informative features is often as important as predictive accuracy, since this can provide mechanistic insight and conceptual understanding.
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.
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.
Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation
The network is then fine-tuned on a combination of real and these newly constructed artificial labeled instances.
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Here we address the problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for rapid automated malaria diagnosis.
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data
Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0. 1 to -0. 05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting.
Model Validation Using Mutated Training Labels: An Exploratory Study
MV mutates training data labels, retrains the model against the mutated data, then uses the metamorphic relation that captures the consequent training performance changes to assess model fit.
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).
Faster Convergence & Generalization in DNNs
Deep neural networks have gained tremendous popularity in last few years.
Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner.
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.
Adaptive Mechanism Design: Learning to Promote Cooperation
In the future, artificial learning agents are likely to become increasingly widespread in our society.
Empirical Risk Minimization under Fairness Constraints
It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable.
Improving Active Learning in Systematic Reviews
The first step in a systematic review task is to identify all the studies relevant to the review.
A Tutorial on Canonical Correlation Methods
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables.
Probabilistic map-matching using particle filters
Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services.
Improved Particle Filters for Vehicle Localisation
The ability to track a moving vehicle is of crucial importance in numerous applications.
Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings
In a traditional setting, classifiers are trained to approximate a target function $f:X \rightarrow Y$ where at least a sample for each $y \in Y$ is presented to the training algorithm.
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.
Learning Shared Representations in Multi-task Reinforcement Learning
We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space.
PinView: Implicit Feedback in Content-Based Image Retrieval
It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user.
PAC-Bayes Analysis of Multi-view Learning
This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers.
Retrieval of Experiments by Efficient Estimation of Marginal Likelihood
To incorporate this information in the retrieval task, we suggest employing a retrieval metric that utilizes probabilistic models learned from the measurements.
Principled Non-Linear Feature Selection
Recent non-linear feature selection approaches employing greedy optimisation of Centred Kernel Target Alignment(KTA) exhibit strong results in terms of generalisation accuracy and sparsity.
Learning Non-Linear Feature Maps
Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology.
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on
Facial Expression Recognition (FER)
on FER2013
MahNMF: Manhattan Non-negative Matrix Factorization
This paper presents Manhattan NMF (MahNMF) which minimizes the Manhattan distance between $X$ and $W^T H$ for modeling the heavy tailed Laplacian noise.
A Note on Improved Loss Bounds for Multiple Kernel Learning
In this paper, we correct an upper bound, presented in~\cite{hs-11}, on the generalisation error of classifiers learned through multiple kernel learning.
