We first theoretically explore the impact of neural quantization on federated knowledge transfer across quantized DNNs and provide the convergence proof of the quantized federated learning.
The project goals and objectives are
1.Communication privacy and security of multi-agent systems:
Develop a privacy-enhanced multi-agent system that uses shared knowledge for both (i) Vision and (ii) Communication tasks.
2.Ego-motion prediction under Intermittent Feedback:
This goal removes the assumption that the GPS signal is always given and considers a GPS denied area. We design a hybrid system to help a traditional error-based control method maintain an error bound, of its state information using a CNN-based localization method.
We are proposing end-to-end video compression with motion field prediction. In video-based point cloud compression (V-PCC), a dynamic point cloud is projected onto geometry and attribute videos patch by patch for compression. We propose a CNN-based occupancy map recovery method to improve the quality of the reconstructed occupancy map video. To the best of our knowledge, this is the first deep learning based accurate occupancy map work for improving V-PCC coding efficiency.
DeepSLAM project aims to Develop an end-to-end deep neural network simultaneously capable of (a) Object Detection, and (b) Re-identification/Tracking
(a) Object Detection using RGB images/videos.
(b) Object Re-identification over multiple frames.
This all-in-one solution provides a better level of information integrity and reuse. In doing so, a local belief of the surrounding area is trained with occupancy grid to generate a local implicit map to capture dynamic road condition. Then local coarse implicit mapping is then combined with global accurate road information for above goals.
Conventional video coding methods optimize each part separately which might lead to sub-optimal solution. Motivated by the success of deep learning on computer vision tasks, we are proposing deep learning for video compression in an end-to-end manner.
The objectives of this research are: (1) Develop a unified and accurate deep learning based regression model for predicting energy usage of different building types. (2) Develop a zero shot learning approach (using an ontology of building types) to accurately predict energy usage of building types whose data are not available during training. (3) Evaluate the deep learning model against traditional machine learning models. If successful, the proposed research can accurately model the energy usage of a variety of building types for architects and engineers (at design time).
DeepSLAM project is designed to develop an end-to-end and all-in-one deep neural network capable of followings, simultaneously, with only road scene RGB images
(a) Object Detection using RGB images and videos;
(b) Object Re-identification over multiple frames, with occlusions;
(c) Predicting multiple object’s future trajectories.
This all-in-one solution provides a better level of information integrity and reuse. In doing so, a local belief of the surrounding area will be trained with grid cells, a navigation system in humans’ brain, to generate a local implicit map to capture dynamic road condition. Then local coarse implicit mapping is then combined with global accurate road information for above three goals.
The goal of this project is to design and develop an intelligent task management for online education and business purpose. The project includes OneTask online platform, self-adaptive neural question generation for study quality evaluation and companion, GCN-based DRL model for self-adaptive neural question generation and study content recommendation and entity-related open-domain question answering system.
The goal of this project is to develop a Deep Neural Network based risk management model that can help financial companies predict loan default likelihood with a higher accuracy when a customer applies for a loan.
The goal of this project is to explore potential vulnerabilities in federated learning applications. Federated learning is a new kind of distributed machine learning with decentralized data. There is no need for data sharing for federated learning.