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Found 18 papers, 3 papers with code
Adaptive Feature Ranking for Unsupervised Transfer Learning
Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain.
Generalising the Discriminative Restricted Boltzmann Machine
Results show that each of the three compared models outperforms the remaining two in one of the three datasets, thus indicating that the proposed theoretical generalisation of the DRBM may be valuable in practice.
Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines
While knowledge representation and reasoning are considered the keys for human-level artificial intelligence, connectionist networks have been shown successful in a broad range of applications due to their capacity for robust learning and flexible inference under uncertainty.
Unsupervised Neural-Symbolic Integration
Symbolic has been long considered as a language of human intelligence while neural networks have advantages of robust computation and dealing with noisy data.
Linear-Time Sequence Classification using Restricted Boltzmann Machines
Also, the experimental results on optical character recognition, part-of-speech tagging and text chunking demonstrate that our model is comparable to recurrent neural networks with complex memory gates while requiring far fewer parameters.
On Multi-resident Activity Recognition in Ambient Smart-Homes
Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue.
ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task
We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recognition (NER) task.
dpUGC: Learn Differentially Private Representation for User Generated Contents
To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing.
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems.
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition
Deep learning has been applied to achieve significant progress in emotion recognition.
Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition
Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making a significant progress in this area.
Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network
Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis.
Hand gesture detection in tests performed by older adults
We have implemented a hand gesture detector to detect the gestures in the hand movement tests and our detection mAP is 0. 782 which is better than the state-of-the-art.
Logical Boltzmann Machines
The idea of representing symbolic knowledge in connectionist systems has been a long-standing endeavour which has attracted much attention recently with the objective of combining machine learning and scalable sound reasoning.
A Comprehensive Review on Deep Supervision: Theories and Applications
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network.
Rapid-Motion-Track: Markerless Tracking of Fast Human Motion with Deeper Learning
RMT and a range of DLC models were applied to the video data with tapping frequencies up to 8Hz to extract movement features.
Machine Learning for Leaf Disease Classification: Data, Techniques and Applications
Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection.
Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches
Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN).
