Shiyu Chang

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: It is demonstrated that the rich content and linkage information in a heterogeneous network can be captured by a multi-resolution deep embedding function, so that similarities among cross-modal data can be measured directly in a common embedding space.

TL;DR: The decision function for verification is proposed to be viewed as a joint model of a distance metric and a locally adaptive thresholding rule, and the inference on the decision function is formulated as a second-order large-margin regularization problem, and an efficient algorithm is provided in its dual from.

TL;DR: This paper proposes a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question, and proposes a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning.

TL;DR: This work proposes a temporal adaptive neural network that can adaptively determine the optimal scale of temporal dependency and reduces the complexity of motion between neighboring frames using a spatial alignment network which is much more robust and efficient than competing alignment methods.