Search Results for author: Son N. Tran

Found 14 papers, 2 papers with code

Logical Boltzmann Machines

no code implementations10 Dec 2021 Son N. Tran, Artur d'Avila Garcez

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.

Inductive logic programming

Hand gesture detection in tests performed by older adults

no code implementations27 Oct 2021 Guan Huang, Son N. Tran, Quan Bai, Jane Alty

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.

Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network

no code implementations10 May 2021 Muhammad Shakaib Iqbal, Hazrat Ali, Son N. Tran, Talha Iqbal

Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis.

Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

no code implementations28 Apr 2020 Dung Nguyen, Duc Thanh Nguyen, Rui Zeng, Thanh Thi Nguyen, Son N. Tran, Thin Nguyen, Sridha Sridharan, Clinton Fookes

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.

Emotion Recognition

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

no code implementations15 May 2019 Artur d'Avila Garcez, Marco Gori, Luis C. Lamb, Luciano Serafini, Michael Spranger, Son N. Tran

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.

Explainable Models

dpUGC: Learn Differentially Private Representation for User Generated Contents

1 code implementation25 Mar 2019 Xuan-Son Vu, Son N. Tran, Lili Jiang

To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing.

ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task

2 code implementations RANLP 2019 Xuan-Son Vu, Thanh Vu, Son N. Tran, Lili Jiang

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.

Named Entity Recognition NER +1

On Multi-resident Activity Recognition in Ambient Smart-Homes

no code implementations18 Jun 2018 Son N. Tran, Qing Zhang, Mohan Karunanithi

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.

Activity Recognition

Linear-Time Sequence Classification using Restricted Boltzmann Machines

no code implementations6 Oct 2017 Son N. Tran, Srikanth Cherla, Artur Garcez, Tillman Weyde

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.

Chunking Classification +4

Unsupervised Neural-Symbolic Integration

no code implementations6 Jun 2017 Son N. Tran

Symbolic has been long considered as a language of human intelligence while neural networks have advantages of robust computation and dealing with noisy data.

Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines

no code implementations31 May 2017 Son N. Tran

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.

Generalising the Discriminative Restricted Boltzmann Machine

no code implementations6 Apr 2016 Srikanth Cherla, Son N. Tran, Tillman Weyde, Artur d'Avila Garcez

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.

Document Classification General Classification

Adaptive Feature Ranking for Unsupervised Transfer Learning

no code implementations21 Dec 2013 Son N. Tran, Artur d'Avila Garcez

Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain.

Transfer Learning

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