Now showing items 1-4 of 4
Semantic representation learning for discourse processing
(Georgia Institute of Technology, 2016-07-21)
Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research and applications in natural language ...
Deep representation learning on hypersphere
(Georgia Institute of Technology, 2020-07-27)
How to efficiently learn discriminative deep features is arguably one of the core problems in deep learning, since it can benefit a lot of downstream tasks such as visual recognition, object detection, semantic segmentation, ...
Learning dynamic processes over graphs
(Georgia Institute of Technology, 2020-07-09)
Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...
Disentangling neural network representations for improved generalization
(Georgia Institute of Technology, 2020-04-24)
Despite the increasingly broad perceptual capabilities of neural networks, applying them to new tasks requires significant engineering effort in data collection and model design. Generally, inductive biases can make this ...