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.
Advancing the state-of-the-art in image/video compression by adopting deep learning methods in prediction, transform, entropy coding and post processing. Develop fresh new coding tools based on deep learning for post processing, reconstruction enhancement. Investigate new pipelines using deep learning for end-to-end image/video compression. Achieve significant coding improvements with applicable computational complexity as well as deliver insights into deep learning video compression for machine consumption, e.g., tracking, segmentation, recognition.
We propose the ModelKB system automating end-to-end model management in deep learning. We will develop a ModelKB prototype that can automatically (1) extract and store the model’s metadata-including its architecture, weights, and configuration; (2) visualize, query, and compare experiments; and (3) reproduce experiments.
DeepCloud is designed as an open software-defined ecosystem for researchers at different levels with the following salient features and transformative impacts. It is one of the first massively scalable multi-tenant open cloud platform with full-fledged building blocks and comprehensive shared stores (app, model, knowledge, data) for deep learning research and applications.
GraphBTM is a topic model which is an unsupervised algorithm to understand documents. It learns to discover the latent representation of documents and produce meaning clustering of words in the same topic. The goal of GraphBTM is to overcome the limitations of the Latent Dirichlet Allocation (LDA) which suffers from the data sparsity problem in short text and Biterm Topic Model (BTM) which claims an insufficient whole-corpus topic distribution.
The goals of this project include developing the ability to use offline, transferred, and real-time learning using various data sources (intermittent state feedback, cloud) to enable SLAM and related image-based estimation methods.
The goals of this project are to design and develop an Intelligent Task Management Platform with task-oriented assistants for commercial and education services, design and develop an open-domain question answering system, and design and develop a deep neural model for question answering with external Knowledge Base.