Search Results for author:
Found 17 papers, 5 papers with code
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations
Contrastive instance discrimination outperforms supervised learning in downstream tasks like image classification and object detection.
Masked Capsule Autoencoders
We propose Masked Capsule Autoencoders (MCAE), the first Capsule Network that utilises pretraining in a self-supervised manner.
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method
Our findings underscore the potential of our proposed methodology in enhancing the operational efficiency and performance of Capsule Networks, paving the way for their application in increasingly complex computational scenarios.
Semantic Positive Pairs for Enhancing Contrastive Instance Discrimination
Self-supervised learning algorithms based on instance discrimination effectively prevent representation collapse and produce promising results in representation learning.
Vanishing Activations: A Symptom of Deep Capsule Networks
This paper extends the investigation to a range of leading Capsule Network architectures, demonstrating that these issues are not confined to the original design.
Non-intrusive Load Monitoring based on Self-supervised Learning
Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.
Causal Effect Estimation using Variational Information Bottleneck
A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.
AREA: Adaptive Reference-set Based Evolutionary Algorithm for Multiobjective Optimisation
The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems.
Trust-Region Variational Inference with Gaussian Mixture Models
For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently.
Transfer Learning for Non-Intrusive Load Monitoring
It is not clear if the method could be generalised or transferred to different domains, e. g., the test data were drawn from a different country comparing to the training data.
Classification of normal/abnormal heart sound recordings based on multi-domain features and back propagation neural network
The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation.
Efficient Gradient-Free Variational Inference using Policy Search
Inference from complex distributions is a common problem in machine learning needed for many Bayesian methods.
Neural Control Variates for Variance Reduction
In statistics and machine learning, approximation of an intractable integration is often achieved by using the unbiased Monte Carlo estimator, but the variances of the estimation are generally high in many applications.
Sequence-to-point learning with neural networks for nonintrusive load monitoring
Interestingly, we systematically show that the convolutional neural networks can inherently learn the signatures of the target appliances, which are automatically added into the model to reduce the identifiability problem.
Latent Bayesian melding for integrating individual and population models
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour.
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Blind source separation problems are difficult because they are inherently unidentifiable, yet the entire goal is to identify meaningful sources.
Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data
Compared to the point estimate algorithms, which only provide single estimates for those parameters, the Bayesian methods are more meaningful and provide credible intervals, which take into account the uncertainty of the inferred interactions of the miRNA and mRNA.
