An analytic end-to-end collaborative learning algorithm by Li, Sitan, Cheah, Chien Chern

“…However, most current deep learning methods are black-box approaches that are more focused on empirical studies. …”
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Goal modelling for deep reinforcement learning agents by Leung, Jonathan, Shen, Zhiqi, Zeng, Zhiwei, Miao, Chunyan

“…The experiments demonstrate that our method is more sample efficient and can obtain higher average rewards than other related methods that incorporate prior human knowledge in similar ways.…”
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Intelligent diagnostic feedback in virtual learning environment by Guo, Ruisheng

“…This research compares and contrasts several methods in order to investigate the effective use of intelligent feedback towards modelling the stages of students’learning. …”
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Structured sparse representations for supervised and unsupervised learning by Zeng, Yijie

“…Specifically, a slice-based online convolutional dictionary learning (SOCDL) method is proposed for unsupervised image processing tasks such as image reconstruction and inpainting. …”
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Machine learning based retinal vessel detection by Li, Hongru

“…In this dissertation, We adopted the traditional CV method based on 2D-matched Filter and deep learning U-NET method, and achieved good segmentation effect. …”
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Learning epipolar geometry from image sequences by Wexler, Y, Fitzgibbon, AW, Zisserman, A

Layer-wise learning framework for deep networks by Yu, Haoyao

“…The case examples demonstrate a rational compromise regarding layer-wise trainability and precision while validating the applicability of the proposed layer-wise learning method to determine the optimal number of layers for real-world scenarios.…”
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Development of a learning system for robot control by Tan, Chee Wee

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Case-based reasoning library for flexible learning by Zhang, Chuan Yu

“…Case-based reasoning can be applied in learning contexts through the collection, building, and indexing of case libraries. …”
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Multi-class classification using deep learning by Cao, Haozhi

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An artificial sensory neuron with tactile perceptual learning by Wan, Changjin, Chen, Geng, Fu, Yangming, Wang, Ming, Matsuhisa, Naoji, Pan, Shaowu, Pan, Liang, Yang, Hui, Wan, Qing, Zhu, Liqiang, Chen, Xiaodong

“…Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.…”
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Multi-Agent Reinforcement Learning for Autonomous Robotics by Vincent, Caroline R.

“…This thesis explores the application of multi-agent reinforcement learning (MARL) to autonomous robotics by teaching a central controller to navigate multiple agents across various environments without collisions. …”
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Towards ILP-based LTLf passive learning by Ielo, A, Law, M, Fionda, V, Ricca, F, De Giacomo, G, De Giacomo, G, Russo, A

“…Inferring a LTL_f formula from a set of example traces, also known as passive learning, is a challenging task for model-based techniques. …”

Optimal persistent monitoring using reinforcement learning by Hu, Litao

“…This project aims to implement Reinforcement Learning (RL) in the multiple targets monitoring simulation with a single agent. …”
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Structured learning of human interactions in TV shows by Patron-Perez, A, Marszalek, M, Reid, I, Zisserman, A

“…We show that inference can be carried out with polynomial complexity in the number of people, and describe an efficient algorithm for this. The method is evaluated on a new dataset comprising 300 video clips acquired from 23 different TV shows and on the benchmark UT--Interaction dataset.…”

Deep learning for object detection and image segmentation by Tan, Yan Hwa

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Multiple object tracking using deep learning by Zhang, Bohan

“…Therefore, in this project, we aim to develop deep learning-based multi-object tracking method for the application of public transportation. …”
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Adaptive function modal learning neural networks by Kang, Miao

“…Modal learning method is a neural network learning term that refers to a single neural network which combines with more than one mode of learning. …”
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Exploring machine learning models for age recognition by Dharmarathne, Chathuranga, Ouazzane, Karim, Shrestha, Subeksha, Fernando, Sandra

“…This journal about exploring a machine learning models that predicts a person's age based on facial images, using both traditional machine learning techniques and neural networks. …”
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Geometry guided supervised representation learning for classification by Li, Yue

“…However, the AE-based representation learning method, FAE-LG, is trained iteratively by using back-propagation (BP) that requires a significant amount of training time. …”
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